dataset stringlengths 1 82 | model_name stringlengths 0 150 | paper_title stringlengths 22 175 | paper_date timestamp[ns]date 2023-05-02 00:00:00 2024-12-12 00:00:00 | paper_url stringlengths 32 35 | code_links listlengths 1 1 | prompts stringlengths 115 330 | answer stringlengths 1 22 | paper_text stringlengths 83 737k | year_bin stringclasses 2
values | benchmark_split stringclasses 1
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AUTSL | HWGAT | Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition | 2024-07-19T00:00:00 | https://arxiv.org/abs/2407.14224v2 | [
"https://github.com/suvajit-patra/sl-hwgat"
] | In the paper 'Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition', what Rank-1 Recognition Rate score did the HWGAT model get on the AUTSL dataset
| 0.9580 | Title: Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition
Abstract: AbstractAutomatic Sign Language (SL) recognition is an important task in the computer vision community. To build a robust SL recognition system, we need a considerable amount of data w... | 2024-2025 | public |
PROTEINS | CIN++ | CIN++: Enhancing Topological Message Passing | 2023-06-06T00:00:00 | https://arxiv.org/abs/2306.03561v1 | [
"https://github.com/twitter-research/cwn"
] | In the paper 'CIN++: Enhancing Topological Message Passing', what Accuracy score did the CIN++ model get on the PROTEINS dataset
| 80.5 | Title: CIN++: Enhancing Topological Message Passing
Abstract: AbstractGraph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling with long-range interactions and lacking a principled approach to mo... | 2023 | public |
Stanford2D3D Panoramic | SFSS-MMSI (RGB+Normal) | Single Frame Semantic Segmentation Using Multi-Modal Spherical Images | 2023-08-18T00:00:00 | https://arxiv.org/abs/2308.09369v1 | [
"https://github.com/sguttikon/SFSS-MMSI"
] | In the paper 'Single Frame Semantic Segmentation Using Multi-Modal Spherical Images', what mIoU score did the SFSS-MMSI (RGB+Normal) model get on the Stanford2D3D Panoramic dataset
| 58.24% | Title: Single Frame Semantic Segmentation Using Multi-Modal Spherical Images
Abstract: AbstractIn recent years, the research community has shown a lot of interest to panoramic images that offer a360∘superscript360{360}^{\circ}directional perspective. Multiple data modalities can be fed, and complimentary characteristi... | 2023 | public |
GTA-to-Avg(Cityscapes,BDD,Mapillary) | ReVT | A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation | 2023-08-25T00:00:00 | https://arxiv.org/abs/2308.13331v1 | [
"https://github.com/ifnspaml/revt"
] | In the paper 'A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation', what mIoU score did the ReVT model get on the GTA-to-Avg(Cityscapes,BDD,Mapillary) dataset
| 50.2 | Title: A Re-Parameterized Vision Transformer (ReVT)for Domain-Generalized Semantic Segmentation
Abstract: AbstractThe task of semantic segmentation requires a model to assign semantic labels to each pixel of an image. However, the performance of such models degrades when deployed in an unseen domain with different dat... | 2023 | public |
ImageNet V2 | HPT | Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06323v1 | [
"https://github.com/vill-lab/2024-aaai-hpt"
] | In the paper 'Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models', what Top-1 accuracy % score did the HPT model get on the ImageNet V2 dataset
| 65.25 | Title: Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
Abstract: AbstractPrompt learning has become a prevalent strategy for adapting vision-language foundation models to downstream tasks. As large language models (LLMs) have emerged, recent studies have explored the use of... | 2023 | public |
InfographicVQA | PaLI-3 (w/ OCR) | PaLI-3 Vision Language Models: Smaller, Faster, Stronger | 2023-10-13T00:00:00 | https://arxiv.org/abs/2310.09199v2 | [
"https://github.com/kyegomez/PALI3"
] | In the paper 'PaLI-3 Vision Language Models: Smaller, Faster, Stronger', what ANLS score did the PaLI-3 (w/ OCR) model get on the InfographicVQA dataset
| 62.4 | Title: PaLI-3 Vision Language Models:Smaller, Faster, Stronger
Abstract: AbstractThis paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) mod... | 2023 | public |
DomainNet | Transadapter | TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation | 2024-12-05T00:00:00 | https://arxiv.org/abs/2412.04073v1 | [
"https://github.com/enesdoruk/TransAdapter"
] | In the paper 'TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation', what Accuracy score did the Transadapter model get on the DomainNet dataset
| 53.7 | Title: TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation
Abstract: AbstractUnsupervised Domain Adaptation (UDA) aims to utilize labeled data from a source domain to solve tasks in an unlabeled target domain, often hindered by significant domain gaps. Traditional CNN-based methods stru... | 2024-2025 | public |
Pendulum-v1 | TLA | Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures | 2023-05-30T00:00:00 | https://arxiv.org/abs/2305.18701v3 | [
"https://github.com/dee0512/Temporally-Layered-Architecture"
] | In the paper 'Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures', what Action Repetition score did the TLA model get on the Pendulum-v1 dataset
| .7032 | Title: Temporally Layered Architecture for Efficient Continuous Control
Abstract: AbstractWe present a temporally layered architecture (TLA) for temporally adaptive control with minimal energy expenditure. The TLA layers a fast and a slow policy together to achieve temporal abstraction that allows each layer to focus ... | 2023 | public |
Citeseer | Graph-MLP + SWA | The Split Matters: Flat Minima Methods for Improving the Performance of GNNs | 2023-06-15T00:00:00 | https://arxiv.org/abs/2306.09121v1 | [
"https://github.com/foisunt/fmms-in-gnns"
] | In the paper 'The Split Matters: Flat Minima Methods for Improving the Performance of GNNs', what Accuracy score did the Graph-MLP + SWA model get on the Citeseer dataset
| 77.99 ± 1.57% | Title: The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
Abstract: AbstractWhen training a Neural Network, it is optimized using the available training data with the hope that it generalizes well to new or unseen testing data. At the same absolute value, a flat minimum in the loss landscape ... | 2023 | public |
SICK | PromptEOL+CSE+OPT-2.7B | Scaling Sentence Embeddings with Large Language Models | 2023-07-31T00:00:00 | https://arxiv.org/abs/2307.16645v1 | [
"https://github.com/kongds/scaling_sentemb"
] | In the paper 'Scaling Sentence Embeddings with Large Language Models', what Spearman Correlation score did the PromptEOL+CSE+OPT-2.7B model get on the SICK dataset
| 0.8129 | Title: Scaling Sentence Embeddings withLarge Language Models
Abstract: Abstract††††\daggerCorresponding Author.Large language models (LLMs) have recently garnered significant interest. With in-context learning, LLMs achieve impressive results in various natural language tasks. However, the application of LLMs to sente... | 2023 | public |
Refer-YouTube-VOS (2021 public validation) | LoSh-R | LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation | 2023-06-14T00:00:00 | https://arxiv.org/abs/2306.08736v3 | [
"https://github.com/linfengyuan1997/losh"
] | In the paper 'LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation', what J&F score did the LoSh-R model get on the Refer-YouTube-VOS (2021 public validation) dataset
| 64.2 | Title: LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation
Abstract: AbstractReferring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains sophisticated description of the inst... | 2023 | public |
COCO-Stuff-81 | CAUSE-MLP (ViT-S/8) | Causal Unsupervised Semantic Segmentation | 2023-10-11T00:00:00 | https://arxiv.org/abs/2310.07379v1 | [
"https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation"
] | In the paper 'Causal Unsupervised Semantic Segmentation', what mIoU score did the CAUSE-MLP (ViT-S/8) model get on the COCO-Stuff-81 dataset
| 19.1 | Title: Causal Unsupervised Semantic Segmentation
Abstract: AbstractUnsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations. With the advent of self-supervised pre-training, various frameworks utilize the pre-trained features to train prediction heads for unsu... | 2023 | public |
Ego4D | EgoVLPv2 | EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone | 2023-07-11T00:00:00 | https://arxiv.org/abs/2307.05463v2 | [
"https://github.com/facebookresearch/EgoVLPv2"
] | In the paper 'EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone', what R@1 IoU=0.3 score did the EgoVLPv2 model get on the Ego4D dataset
| 12.95 | Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged.◄Feelinglucky?ConversionreportReportan issueView originalon arXiv►CopyrightPrivacy PolicyGenerated on Wed Feb 28 17:27:58 2024 byLaTeXML
Conversion to HTML had a Fatal error and exited abruptly. This document may be tr... | 2023 | public |
EuRoC MAV | CIVO | Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach | 2023-12-20T00:00:00 | https://arxiv.org/abs/2312.13162v1 | [
"https://github.com/habib-Boloorchi/CIVO-Visual-Odometry-"
] | In the paper 'Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach', what Relative Position Error Translation [cm] score did the CIVO model get on the EuRoC MAV dataset
| 1.3574 | Title: Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach
Abstract: AbstractIn this study, we address the critical challenge of balancing speed
and accuracy while maintaining interpretablity in visual odometry (VO)
systems, a pivotal aspect in the field of autonom... | 2023 | public |
GMOT-40 | iGDINO MAC-SORT | TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT | 2024-09-04T00:00:00 | https://arxiv.org/abs/2409.02490v1 | [
"https://github.com/Fsoft-AIC/TP-GMOT"
] | In the paper 'TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT', what mAP@0.5 score did the iGDINO MAC-SORT model get on the GMOT-40 dataset
| 72.7 | Title: TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
Abstract: AbstractWhile Multi-Object Tracking (MOT) has made substantial advancements, it is limited by heavy reliance on prior knowledge and limited to predefined categories. In contrast, Generic Multiple Object ... | 2024-2025 | public |
UMVM-oea-en-fr | UMAEA (w/o surf) | Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment | 2023-07-30T00:00:00 | https://arxiv.org/abs/2307.16210v2 | [
"https://github.com/zjukg/umaea"
] | In the paper 'Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment', what Hits@1 score did the UMAEA (w/o surf) model get on the UMVM-oea-en-fr dataset
| 0.895 | Title: Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-ModalEntity Alignment
Abstract: AbstractAs a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual informati... | 2023 | public |
MORPH Album2 (SE) | ResNet-50-Unimodal-Concentrated | A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04570v3 | [
"https://github.com/paplhjak/facial-age-estimation-benchmark"
] | In the paper 'A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark', what MAE score did the ResNet-50-Unimodal-Concentrated model get on the MORPH Album2 (SE) dataset
| 2.78 | Title: A Call to Reflect on Evaluation Practices for Age Estimation:Comparative Analysis of the State-of-the-Art and a Unified Benchmark
Abstract: AbstractComparing different age estimation methods poses a challenge due to the unreliability of published results stemming from inconsistencies in the benchmarking process... | 2023 | public |
genius | GESN | Addressing Heterophily in Node Classification with Graph Echo State Networks | 2023-05-14T00:00:00 | https://arxiv.org/abs/2305.08233v2 | [
"https://github.com/dtortorella/addressing-heterophily-gesn"
] | In the paper 'Addressing Heterophily in Node Classification with Graph Echo State Networks', what 1:1 Accuracy score did the GESN model get on the genius dataset
| 91.72 ± 0.08 | Title: Addressing Heterophily in Node Classification with Graph Echo State Networks
Abstract: AbstractNode classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node’s neighbourhood. While effective on gr... | 2023 | public |
Oxford-IIIT Pet Dataset | DePT | DePT: Decoupled Prompt Tuning | 2023-09-14T00:00:00 | https://arxiv.org/abs/2309.07439v2 | [
"https://github.com/koorye/dept"
] | In the paper 'DePT: Decoupled Prompt Tuning', what Harmonic mean score did the DePT model get on the Oxford-IIIT Pet Dataset dataset
| 96.37 | Title: DePT: Decoupled Prompt Tuning
Abstract: AbstractThis work breaks through the Base-New Tradeoff (BNT) dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to new tasks, and vice versa. Specifically, through an in-depth analysis of the learn... | 2023 | public |
Office-Home | WAKD (Resnet-18) | Weight Averaging Improves Knowledge Distillation under Domain Shift | 2023-09-20T00:00:00 | https://arxiv.org/abs/2309.11446v1 | [
"https://github.com/vorobeevich/distillation-in-dg"
] | In the paper 'Weight Averaging Improves Knowledge Distillation under Domain Shift', what Average Accuracy score did the WAKD (Resnet-18) model get on the Office-Home dataset
| 66.7 | Title: Weight Averaging Improves Knowledge Distillation under Domain Shift
Abstract: AbstractKnowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mimic a larger teacher network. While it is widel... | 2023 | public |
FreiHAND | Zhou et al. | A Simple Baseline for Efficient Hand Mesh Reconstruction | 2024-03-04T00:00:00 | https://arxiv.org/abs/2403.01813v1 | [
"https://github.com/patiencefromzhou/simplehand"
] | In the paper 'A Simple Baseline for Efficient Hand Mesh Reconstruction', what PA-MPVPE score did the Zhou et al. model get on the FreiHAND dataset
| 6.0 | Title: A Simple Baseline for Efficient Hand Mesh Reconstruction
Abstract: AbstractHand mesh reconstruction has attracted considerable attention in recent years, with various approaches and techniques being proposed. Some of these methods incorporate complex components and designs, which, while effective, may complicat... | 2024-2025 | public |
Filosax | Beat This! | Beat this! Accurate beat tracking without DBN postprocessing | 2024-07-31T00:00:00 | https://arxiv.org/abs/2407.21658v1 | [
"https://github.com/CPJKU/beat_this"
] | In the paper 'Beat this! Accurate beat tracking without DBN postprocessing', what F1 score did the Beat This! model get on the Filosax dataset
| 99.5 | Title: Beat this!Accurate beat tracking without DBN postprocessing
Abstract: AbstractWe propose a system for tracking beats and downbeats with two objectives: generality across a diverse music range, and high accuracy. We achieve generality by training on multiple datasets – including solo instrument recordings, piece... | 2024-2025 | public |
LLFF | Chat-Edit-3D | Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts | 2024-07-09T00:00:00 | https://arxiv.org/abs/2407.06842v2 | [
"https://github.com/Fangkang515/CE3D"
] | In the paper 'Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts', what CLIP score did the Chat-Edit-3D model get on the LLFF dataset
| 0.9 | Title: Chat-Edit-3D: Interactive 3D Scene Editingvia Text Prompts
Abstract: AbstractRecent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still exhibit certain shortcomings, hi... | 2024-2025 | public |
VQA v2 test-dev | CuMo-7B | CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts | 2024-05-09T00:00:00 | https://arxiv.org/abs/2405.05949v1 | [
"https://github.com/shi-labs/cumo"
] | In the paper 'CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts', what Accuracy score did the CuMo-7B model get on the VQA v2 test-dev dataset
| 82.2 | Title: CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
Abstract: AbstractRecent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on multimodal tasks. However, these scaling approaches ... | 2024-2025 | public |
SMAC 26m_vs_30m | QMIX | A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning | 2023-06-04T00:00:00 | https://arxiv.org/abs/2306.02430v1 | [
"https://github.com/j3soon/dfac-extended"
] | In the paper 'A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning', what Median Win Rate score did the QMIX model get on the SMAC 26m_vs_30m dataset
| 62.78 | Title: A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
Abstract: AbstractIn fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing pol... | 2023 | public |
CSL-Daily | TCNet | TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions | 2024-03-18T00:00:00 | https://arxiv.org/abs/2403.11818v1 | [
"https://github.com/hotfinda/tcnet"
] | In the paper 'TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions', what Word Error Rate (WER) score did the TCNet model get on the CSL-Daily dataset
| 29.3 | Title: TCNet: Continuous Sign Language Recognition fromTrajectories and Correlated Regions
Abstract: AbstractA key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input. To address this challenge, we propose TCNet, a hybrid net... | 2024-2025 | public |
Food-101N | LRA-diffusion (CLIP ViT) | Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19518v2 | [
"https://github.com/puar-playground/lra-diffusion"
] | In the paper 'Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels', what Accuracy score did the LRA-diffusion (CLIP ViT) model get on the Food-101N dataset
| 93.42 | Title: Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Abstract: AbstractLearning from noisy labels is a long-standing problem in machine learning for real applications. One of the main research lines focuses on learning a label corrector to purify potential noisy labels. However, these metho... | 2023 | public |
MUSES: MUlti-SEnsor Semantic perception dataset | MUSES (Mask2Former /w 4xSwin-T) | MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty | 2024-01-23T00:00:00 | https://arxiv.org/abs/2401.12761v4 | [
"https://github.com/timbroed/MUSES"
] | In the paper 'MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty', what PQ score did the MUSES (Mask2Former /w 4xSwin-T) model get on the MUSES: MUlti-SEnsor Semantic perception dataset dataset
| 53.6 | Title: MUSES: The Multi-Sensor Semantic Perception Datasetfor Driving under Uncertainty
Abstract: AbstractAchieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions. However, existing seman... | 2024-2025 | public |
CDD Dataset (season-varying) | C2FNet | C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images | 2024-04-22T00:00:00 | https://arxiv.org/abs/2404.13838v1 | [
"https://github.com/chengxihan/c2f-semicd-and-c2f-cdnet"
] | In the paper 'C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images', what F1-Score score did the C2FNet model get on the CDD Dataset (season-varying) dataset
| 95.93 | Warning: Very short content extracted (0 chars). URL may not be properly formatted. | 2024-2025 | public |
AudioCaps | CLAPSep | CLAPSep: Leveraging Contrastive Pre-trained Model for Multi-Modal Query-Conditioned Target Sound Extraction | 2024-02-27T00:00:00 | https://arxiv.org/abs/2402.17455v4 | [
"https://github.com/aisaka0v0/clapsep"
] | In the paper 'CLAPSep: Leveraging Contrastive Pre-trained Model for Multi-Modal Query-Conditioned Target Sound Extraction', what SI-SDRi score did the CLAPSep model get on the AudioCaps dataset
| 9.40 | Title: CLAPSep: Leveraging Contrastive Pre-trained Models for Multi-Modal Query-Conditioned Target Sound Extraction
Abstract: AbstractUniversal sound separation (USS) aims to extract arbitrary types of sounds from real-world sound recordings. Language-queried target sound extraction (TSE) is an effective approach to a... | 2024-2025 | public |
Nordland | SelaVPR | Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14505v3 | [
"https://github.com/Lu-Feng/SelaVPR"
] | In the paper 'Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition', what Recall@1 score did the SelaVPR model get on the Nordland dataset
| 86.6 | Title: Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
Abstract: AbstractRecent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attem... | 2024-2025 | public |
MM-Vet | CoLLaVO | CoLLaVO: Crayon Large Language and Vision mOdel | 2024-02-17T00:00:00 | https://arxiv.org/abs/2402.11248v4 | [
"https://github.com/ByungKwanLee/CoLLaVO"
] | In the paper 'CoLLaVO: Crayon Large Language and Vision mOdel', what GPT-4 score score did the CoLLaVO model get on the MM-Vet dataset
| 40.3 | Title: CoLLaVO: Crayon Large Language and Vision mOdel
Abstract: AbstractThe remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet, it remains unexplored whether current VLMs genuinely possess quali... | 2024-2025 | public |
EconLogicQA | Gemma-2B-IT | EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07938v2 | [
"https://github.com/yinzhu-quan/lm-evaluation-harness"
] | In the paper 'EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning', what Accuracy score did the Gemma-2B-IT model get on the EconLogicQA dataset
| 0.0846 | Title: EconLogicQA: A Question-Answering Benchmark for EvaluatingLarge Language Models in Economic Sequential Reasoning
Abstract: AbstractIn this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms of... | 2024-2025 | public |
FSC147 | SemAug-SAFECount | Semantic Generative Augmentations for Few-Shot Counting | 2023-10-26T00:00:00 | https://arxiv.org/abs/2311.16122v1 | [
"https://github.com/perladoubinsky/SemAug"
] | In the paper 'Semantic Generative Augmentations for Few-Shot Counting', what MAE(val) score did the SemAug-SAFECount model get on the FSC147 dataset
| 12.59 | Title: Semantic Generative Augmentations for Few-Shot Counting
Abstract: AbstractWith the availability of powerful text-to-image diffusion models, recent works have explored the use of synthetic data to improve image classification performances. These works show that it can effectively augment or even replace real dat... | 2023 | public |
NTU RGB+D | π-ViT (RGB + Pose) | Just Add $π$! Pose Induced Video Transformers for Understanding Activities of Daily Living | 2023-11-30T00:00:00 | https://arxiv.org/abs/2311.18840v1 | [
"https://github.com/dominickrei/pi-vit"
] | In the paper 'Just Add $π$! Pose Induced Video Transformers for Understanding Activities of Daily Living', what Accuracy (CS) score did the π-ViT (RGB + Pose) model get on the NTU RGB+D dataset
| 96.3 | Title: Just Add𝝅𝝅\boldsymbol{\pi}!PoseInduced Video Transformers forUnderstanding Activities of Daily Living
Abstract: AbstractVideo transformers have become the de facto standard for human action recognition, yet their exclusive reliance on the RGB modality still limits their adoption in certain domains. One such d... | 2023 | public |
Slovo: Russian Sign Language Dataset | mVITv2-S | Slovo: Russian Sign Language Dataset | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.14527v3 | [
"https://github.com/hukenovs/slovo"
] | In the paper 'Slovo: Russian Sign Language Dataset', what Mean Accuracy score did the mVITv2-S model get on the Slovo: Russian Sign Language Dataset dataset
| 64.09 | Title: Slovo: Russian Sign Language Dataset
Abstract: AbstractOne of the main challenges of the sign language recognition task is the difficulty of collecting a suitable dataset due to the gap between hard-of-hearing and hearing societies. In addition, the sign language in each country differs significantly, which obl... | 2023 | public |
MM-Vet | VisionZip (Retain 192 Tokens) | VisionZip: Longer is Better but Not Necessary in Vision Language Models | 2024-12-05T00:00:00 | https://arxiv.org/abs/2412.04467v1 | [
"https://github.com/dvlab-research/visionzip"
] | In the paper 'VisionZip: Longer is Better but Not Necessary in Vision Language Models', what GPT-4 score score did the VisionZip (Retain 192 Tokens) model get on the MM-Vet dataset
| 31.7 | Title: VisionZip: Longer is Better but Not Necessary in Vision Language Models
Abstract: AbstractRecent advancements in vision-language models have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raising computational costs. However, we observe... | 2024-2025 | public |
Automatic Cardiac Diagnosis Challenge (ACDC) | MERIT-GCASCADE | G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation | 2023-10-24T00:00:00 | https://arxiv.org/abs/2310.16175v1 | [
"https://github.com/SLDGroup/G-CASCADE"
] | In the paper 'G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation', what Avg DSC score did the MERIT-GCASCADE model get on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset
| 92.23 | Title: G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
Abstract: AbstractIn recent years, medical image segmentation has become an important application in the field of computer-aided diagnosis. In this paper, we are the first to propose a new graph convolution-based decode... | 2023 | public |
SST-2 | OPT-125M | Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization | 2024-05-24T00:00:00 | https://arxiv.org/abs/2405.15861v3 | [
"https://github.com/ZidongLiu/DeComFL"
] | In the paper 'Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization', what Test Accuracy score did the OPT-125M model get on the SST-2 dataset
| 85.08% | Title: Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Abstract: AbstractFederated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communication costs associated wit... | 2024-2025 | public |
UCF-101 | ACDiT | ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer | 2024-12-10T00:00:00 | https://arxiv.org/abs/2412.07720v1 | [
"https://github.com/thunlp/acdit"
] | In the paper 'ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer', what FVD16 score did the ACDiT model get on the UCF-101 dataset
| 90 | Title: ACDiT: Interpolating Autoregressive Conditional Modeling andDiffusion Transformer
Abstract: AbstractThe recent surge of interest in comprehensive multimodal models has necessitated the unification of diverse modalities. However, the unification suffers from disparate methodologies. Continuous visual generation ... | 2024-2025 | public |
BBBP | G-Tuning | Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns | 2023-12-21T00:00:00 | https://arxiv.org/abs/2312.13583v1 | [
"https://github.com/zjunet/G-Tuning"
] | In the paper 'Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns', what ROC-AUC score did the G-Tuning model get on the BBBP dataset
| 72.59 | Title: Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
Abstract: AbstractRecently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generally attributed to the structural consisten... | 2023 | public |
WikiText-103 | Transformer+SSA | The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01705v1 | [
"https://github.com/shamim-hussain/ssa"
] | In the paper 'The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles', what Validation perplexity score did the Transformer+SSA model get on the WikiText-103 dataset
| 16.91 | Title: The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Abstract: Abstract.Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible connectivity patterns increases exp... | 2023 | public |
SVT | CLIP4STR-L | CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.14014v3 | [
"https://github.com/VamosC/CLIP4STR"
] | In the paper 'CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model', what Accuracy score did the CLIP4STR-L model get on the SVT dataset
| 98.5 | Title: CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
Abstract: AbstractPre-trained vision-language models (VLMs) are the de-facto foundation models for various downstream tasks. However, scene text recognition methods still prefer backbones pre-trained on a single modali... | 2023 | public |
GoPro | CGNet | CascadedGaze: Efficiency in Global Context Extraction for Image Restoration | 2024-01-26T00:00:00 | https://arxiv.org/abs/2401.15235v2 | [
"https://github.com/Ascend-Research/CascadedGaze"
] | In the paper 'CascadedGaze: Efficiency in Global Context Extraction for Image Restoration', what PSNR score did the CGNet model get on the GoPro dataset
| 33.77 | Title: CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Abstract: AbstractImage restoration tasks traditionally rely on convolutional neural networks. However, given the local nature of the convolutional operator, they struggle to capture global information. The promise of attention mechanis... | 2024-2025 | public |
PCQM4Mv2-LSC | EGT+SSA | The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01705v1 | [
"https://github.com/shamim-hussain/ssa"
] | In the paper 'The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles', what Validation MAE score did the EGT+SSA model get on the PCQM4Mv2-LSC dataset
| 0.0876 | Title: The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Abstract: Abstract.Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible connectivity patterns increases exp... | 2023 | public |
RAF-DB | ResEmoteNet | ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition | 2024-09-01T00:00:00 | https://arxiv.org/abs/2409.10545v2 | [
"https://github.com/ArnabKumarRoy02/ResEmoteNet"
] | In the paper 'ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition', what Overall Accuracy score did the ResEmoteNet model get on the RAF-DB dataset
| 94.76 | Title: ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
Abstract: AbstractThe human face is a silent communicator, expressing emotions and thoughts through it’s facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made si... | 2024-2025 | public |
GSM8K | MathCoder-CL-7B | MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03731v1 | [
"https://github.com/mathllm/mathcoder"
] | In the paper 'MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning', what Accuracy score did the MathCoder-CL-7B model get on the GSM8K dataset
| 67.8 | Title: MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
Abstract: AbstractThe recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason with natural language, generate c... | 2023 | public |
NExT-QA (Open-ended VideoQA) | MovieChat+ | MovieChat+: Question-aware Sparse Memory for Long Video Question Answering | 2024-04-26T00:00:00 | https://arxiv.org/abs/2404.17176v1 | [
"https://github.com/rese1f/MovieChat"
] | In the paper 'MovieChat+: Question-aware Sparse Memory for Long Video Question Answering', what Accuracy score did the MovieChat+ model get on the NExT-QA (Open-ended VideoQA) dataset
| 54.8 | Title: MovieChat+: Question-aware Sparse Memory for Long Video Question Answering
Abstract: AbstractRecently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing methods either employ comple... | 2024-2025 | public |
CIFAR10 100k | GraphGPS + HDSE | Enhancing Graph Transformers with Hierarchical Distance Structural Encoding | 2023-08-22T00:00:00 | https://arxiv.org/abs/2308.11129v4 | [
"https://github.com/luoyk1999/hdse"
] | In the paper 'Enhancing Graph Transformers with Hierarchical Distance Structural Encoding', what Accuracy (%) score did the GraphGPS + HDSE model get on the CIFAR10 100k dataset
| 76.180±0.277 | Title: Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
Abstract: AbstractGraph transformers need strong inductive biases to derive meaningful attention scores. Yet, current methods often fall short in capturing longer ranges, hierarchical structures, or community structures, which are commo... | 2023 | public |
cifar100 | ResNet50 | Guarding Barlow Twins Against Overfitting with Mixed Samples | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.02151v1 | [
"https://github.com/wgcban/mix-bt"
] | In the paper 'Guarding Barlow Twins Against Overfitting with Mixed Samples', what average top-1 classification accuracy score did the ResNet50 model get on the cifar100 dataset
| 72.51 | Title: Guarding Barlow Twins Against Overfitting with Mixed Samples
Abstract: AbstractSelf-supervised Learning (SSL) aims to learn transferable feature representations for downstream applications without relying on labeled data. The Barlow Twins algorithm, renowned for its widespread adoption and straightforward imple... | 2023 | public |
ETTh1 (336) Multivariate | GridTST | Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers | 2024-05-22T00:00:00 | https://arxiv.org/abs/2405.13810v1 | [
"https://github.com/Hannibal046/GridTST"
] | In the paper 'Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers', what MSE score did the GridTST model get on the ETTh1 (336) Multivariate dataset
| 0.436 | Title: Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers
Abstract: AbstractTime series prediction is crucial for understanding and forecasting complex dynamics in various domains, ranging from finance and economics to climate and healthcare. Based on Transformer architecture, on... | 2024-2025 | public |
Cholec80 | MSN | Self-Supervised Learning for Endoscopic Video Analysis | 2023-08-23T00:00:00 | https://arxiv.org/abs/2308.12394v1 | [
"https://github.com/royhirsch/endossl"
] | In the paper 'Self-Supervised Learning for Endoscopic Video Analysis', what F1 score did the MSN model get on the Cholec80 dataset
| 89.6 | Title: Self-Supervised Learningfor Endoscopic Video Analysis
Abstract: AbstractSelf-supervised learning (SSL) has led to important breakthroughs in computer vision by allowing learning from large amounts ofunlabeleddata. As such, it might have a pivotal role to play in biomedicine where annotating data requires a high... | 2023 | public |
FRMT (Chinese - Taiwan) | PaLM 2 | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what BLEURT score did the PaLM 2 model get on the FRMT (Chinese - Taiwan) dataset
| 72.0 | Title: PaLM 2 Technical Report
Abstract: AbstractWe introduce PaLM 2, a new state-of-the-art language model that has better multilingual
and reasoning capabilities and is more compute-efficient than its
predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evalu... | 2023 | public |
Weather2K114 (96) | MoLE-DLinear | Mixture-of-Linear-Experts for Long-term Time Series Forecasting | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06786v3 | [
"https://github.com/rogerni/mole"
] | In the paper 'Mixture-of-Linear-Experts for Long-term Time Series Forecasting', what MSE score did the MoLE-DLinear model get on the Weather2K114 (96) dataset
| 0.391 | Title: Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Abstract: AbstractLong-term time series forecasting (LTSF) aims to predict future values of a time series given the past values. The current state-of-the-art (SOTA) on this problem is attained in some cases bylinear-centricmodels, which primarily f... | 2023 | public |
CIFAR-100-LT (ρ=100) | DirMixE | Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07780v1 | [
"https://github.com/scongl/dirmixe"
] | In the paper 'Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition', what Error Rate score did the DirMixE model get on the CIFAR-100-LT (ρ=100) dataset
| 51.62 | Title: Harnessing Hierarchical Label Distribution Variationsin Test Agnostic Long-tail Recognition
Abstract: AbstractThis paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the variation in these dist... | 2024-2025 | public |
waymo cyclist | PillarNeXt | PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds | 2023-05-08T00:00:00 | https://arxiv.org/abs/2305.04925v1 | [
"https://github.com/qcraftai/pillarnext"
] | In the paper 'PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds', what APH/L2 score did the PillarNeXt model get on the waymo cyclist dataset
| 70.55 | Title: PillarNeXt: Rethinking Network Designs for 3D Object Detectionin LiDAR Point Clouds
Abstract: AbstractIn order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object detection research mostly focuses on designing dedicated local point aggregators for fine-grained geometrical modeling. ... | 2023 | public |
Kvasir-SEG | EffiSegNet-B4 | EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder | 2024-07-23T00:00:00 | https://arxiv.org/abs/2407.16298v1 | [
"https://github.com/ivezakis/effisegnet"
] | In the paper 'EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder', what mean Dice score did the EffiSegNet-B4 model get on the Kvasir-SEG dataset
| 0.9483 | Title: EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder††thanks:Funded by the European Union (DIOPTRA, 101096649). Views and
opinions expressed are, however, those of the author(s) only and do not
necessarily reflect those of the European Union o... | 2024-2025 | public |
UTKFace | MiVOLO-D1 | MiVOLO: Multi-input Transformer for Age and Gender Estimation | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04616v2 | [
"https://github.com/wildchlamydia/mivolo"
] | In the paper 'MiVOLO: Multi-input Transformer for Age and Gender Estimation', what MAE score did the MiVOLO-D1 model get on the UTKFace dataset
| 3.7 | Title: MiVOLO: Multi-input Transformer for Age and Gender Estimation
Abstract: AbstractAge and gender recognition in the wild is a highly challenging task: apart from the variability of conditions, pose complexities, and varying image quality, there are cases where the face is partially or completely occluded. We pres... | 2023 | public |
UMVM-oea-en-de | UMAEA (w/o surf & iter ) | Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment | 2023-07-30T00:00:00 | https://arxiv.org/abs/2307.16210v2 | [
"https://github.com/zjukg/umaea"
] | In the paper 'Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment', what Hits@1 score did the UMAEA (w/o surf & iter ) model get on the UMVM-oea-en-de dataset
| 0.956 | Title: Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-ModalEntity Alignment
Abstract: AbstractAs a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual informati... | 2023 | public |
DanceTrack | UCMCTrack | UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.08952v2 | [
"https://github.com/corfyi/ucmctrack"
] | In the paper 'UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation', what HOTA score did the UCMCTrack model get on the DanceTrack dataset
| 63.6 | Title: UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation
Abstract: AbstractMulti-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous... | 2023 | public |
CIFAR10 100k | GRED | Recurrent Distance Filtering for Graph Representation Learning | 2023-12-03T00:00:00 | https://arxiv.org/abs/2312.01538v3 | [
"https://github.com/skeletondyh/gred"
] | In the paper 'Recurrent Distance Filtering for Graph Representation Learning', what Accuracy (%) score did the GRED model get on the CIFAR10 100k dataset
| 76.853±0.185 | Title: Recurrent Distance Filtering for Graph Representation Learning
Abstract: AbstractGraph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing the information from distant nodes effectively. Conversely, graph transformers allow each node to attend to all other nodes ... | 2023 | public |
UCSD Ped2 | AnomalyRuler | Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models | 2024-07-14T00:00:00 | https://arxiv.org/abs/2407.10299v2 | [
"https://github.com/Yuchen413/AnomalyRuler"
] | In the paper 'Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models', what AUC score did the AnomalyRuler model get on the UCSD Ped2 dataset
| 97.9% | Title: Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
Abstract: AbstractVideo Anomaly Detection (VAD) is crucial for applications such as security surveillance and autonomous driving. However, existing VAD methods provide little rationale behind detection, hindering public trust in ... | 2024-2025 | public |
MLO-Cn2 | Persistence | Effective Benchmarks for Optical Turbulence Modeling | 2024-01-07T00:00:00 | https://arxiv.org/abs/2401.03573v1 | [
"https://github.com/cdjellen/otbench"
] | In the paper 'Effective Benchmarks for Optical Turbulence Modeling', what RMSE score did the Persistence model get on the MLO-Cn2 dataset
| 1.227 | Title: Effective Benchmarks for Optical Turbulence Modeling
Abstract: AbstractOptical turbulence presents a significant challenge for communication, directed energy, and imaging systems, especially in the atmospheric boundary layer. Effective modeling of optical turbulence strength is critical for the development and ... | 2024-2025 | public |
LAMBADA | PaLM 2-S (one-shot) | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what Accuracy score did the PaLM 2-S (one-shot) model get on the LAMBADA dataset
| 80.7 | Title: PaLM 2 Technical Report
Abstract: AbstractWe introduce PaLM 2, a new state-of-the-art language model that has better multilingual
and reasoning capabilities and is more compute-efficient than its
predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evalu... | 2023 | public |
ColonINST-v1 (Seen) | ColonGPT (w/ LoRA, w/o extra data) | Frontiers in Intelligent Colonoscopy | 2024-10-22T00:00:00 | https://arxiv.org/abs/2410.17241v1 | [
"https://github.com/ai4colonoscopy/intelliscope"
] | In the paper 'Frontiers in Intelligent Colonoscopy', what Accuray score did the ColonGPT (w/ LoRA, w/o extra data) model get on the ColonINST-v1 (Seen) dataset
| 99.02 | Title: Frontiers in Intelligent Colonoscopy
Abstract: AbstractColonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. With this goal, w... | 2024-2025 | public |
Squirrel | FaberNet | HoloNets: Spectral Convolutions do extend to Directed Graphs | 2023-10-03T00:00:00 | https://arxiv.org/abs/2310.02232v2 | [
"https://github.com/ChristianKoke/HoloNets"
] | In the paper 'HoloNets: Spectral Convolutions do extend to Directed Graphs', what Accuracy score did the FaberNet model get on the Squirrel dataset
| 76.71±1.92 | Title: HoloNets: Spectral Convolutions do extend to Directed Graphs
Abstract: AbstractWithin the graph learning community, conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs: Only there could the existence of a well-defined graph Fourier transform be guaranteed,... | 2023 | public |
STS13 | PromptEOL+CSE+OPT-13B | Scaling Sentence Embeddings with Large Language Models | 2023-07-31T00:00:00 | https://arxiv.org/abs/2307.16645v1 | [
"https://github.com/kongds/scaling_sentemb"
] | In the paper 'Scaling Sentence Embeddings with Large Language Models', what Spearman Correlation score did the PromptEOL+CSE+OPT-13B model get on the STS13 dataset
| 0.9024 | Title: Scaling Sentence Embeddings withLarge Language Models
Abstract: Abstract††††\daggerCorresponding Author.Large language models (LLMs) have recently garnered significant interest. With in-context learning, LLMs achieve impressive results in various natural language tasks. However, the application of LLMs to sente... | 2023 | public |
ASTE | MvP | MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.12627v1 | [
"https://github.com/ZubinGou/multi-view-prompting"
] | In the paper 'MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction', what F1 (L14) score did the MvP model get on the ASTE dataset
| 63.33 | Title: MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
Abstract: AbstractGenerativemethods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ign... | 2023 | public |
Weather (720) | DiPE-Linear | Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting | 2024-11-26T00:00:00 | https://arxiv.org/abs/2411.17257v1 | [
"https://github.com/wintertee/dipe-linear"
] | In the paper 'Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting', what MSE score did the DiPE-Linear model get on the Weather (720) dataset
| 0.306 | Title: Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting
Abstract: AbstractIndustry 5.0 introduces new challenges for Long-term Time Series Forecasting (LTSF), characterized by high-dimensional, high-resolution data and high-stakes application scenarios. Against this backdrop, d... | 2024-2025 | public |
SoundingEarth | GeoCLAP | Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping | 2023-09-19T00:00:00 | https://arxiv.org/abs/2309.10667v1 | [
"https://github.com/mvrl/geoclap"
] | In the paper 'Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping', what Median Rank score did the GeoCLAP model get on the SoundingEarth dataset
| 159 | Title: Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping
Abstract: AbstractWe focus on the task of soundscape mapping, which involves predicting the most probable sounds that could be perceived at a particular geographic location. We utilise recent state-of-the-art models to encode geotagged audio, a text... | 2023 | public |
FineDiving | RICA^2 (Deterministic) | RICA2: Rubric-Informed, Calibrated Assessment of Actions | 2024-08-04T00:00:00 | https://arxiv.org/abs/2408.02138v2 | [
"https://github.com/abrarmajeedi/rica2_aqa"
] | In the paper 'RICA2: Rubric-Informed, Calibrated Assessment of Actions', what Spearman Correlation score did the RICA^2 (Deterministic) model get on the FineDiving dataset
| 0.9421 | Title: RICA2: Rubric-Informed, CalibratedAssessment of Actions
Abstract: AbstractThe ability to quantify how well an action is carried out, also known as action quality assessment (AQA), has attracted recent interest in the vision community. Unfortunately, prior methods often ignore the score rubric used by human expe... | 2024-2025 | public |
MixSNIPS | MISCA | MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention | 2023-12-10T00:00:00 | https://arxiv.org/abs/2312.05741v1 | [
"https://github.com/vinairesearch/misca"
] | In the paper 'MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention', what Accuracy score did the MISCA model get on the MixSNIPS dataset
| 97.3 | Title: MISCA: A Joint Model for Multiple Intent Detection and Slot Fillingwith Intent-Slot Co-Attention
Abstract: AbstractThe research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations. Recent advanced approaches, which are join... | 2023 | public |
WikiText-103 | Transformer+SSA+Self-ensemble | The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01705v1 | [
"https://github.com/shamim-hussain/ssa"
] | In the paper 'The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles', what Validation perplexity score did the Transformer+SSA+Self-ensemble model get on the WikiText-103 dataset
| 16.54 | Title: The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Abstract: Abstract.Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible connectivity patterns increases exp... | 2023 | public |
PCQM4Mv2-LSC | GPTrans-T | Graph Propagation Transformer for Graph Representation Learning | 2023-05-19T00:00:00 | https://arxiv.org/abs/2305.11424v3 | [
"https://github.com/czczup/gptrans"
] | In the paper 'Graph Propagation Transformer for Graph Representation Learning', what Validation MAE score did the GPTrans-T model get on the PCQM4Mv2-LSC dataset
| 0.0833 | Title: IJCAI–23 Formatting Instructions
Abstract: AbstractThis paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks... | 2023 | public |
PubMed with Public Split: fixed 20 nodes per class | GGCM | From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited | 2023-09-24T00:00:00 | https://arxiv.org/abs/2309.13599v2 | [
"https://github.com/zhengwang100/ogc_ggcm"
] | In the paper 'From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited', what Accuracy score did the GGCM model get on the PubMed with Public Split: fixed 20 nodes per class dataset
| 80.8% | Title: From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Abstract: AbstractGraph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networ... | 2023 | public |
Intel Image Classification | ResNet-18 + Vision Eagle Attention | Vision Eagle Attention: a new lens for advancing image classification | 2024-11-15T00:00:00 | https://arxiv.org/abs/2411.10564v2 | [
"https://github.com/MahmudulHasan11085/Vision-Eagle-Attention"
] | In the paper 'Vision Eagle Attention: a new lens for advancing image classification', what Accuracy score did the ResNet-18 + Vision Eagle Attention model get on the Intel Image Classification dataset
| 92.43 | Title: Vision Eagle Attention: A New Lens for Advancing Image Classification
Abstract: AbstractIn computer vision tasks, the ability to focus on relevant regions within an image is crucial for improving model performance, particularly when key features are small, subtle, or spatially dispersed. Convolutional neural ne... | 2024-2025 | public |
SIR^2(Objects) | DSRNet | Single Image Reflection Separation via Component Synergy | 2023-08-19T00:00:00 | https://arxiv.org/abs/2308.10027v1 | [
"https://github.com/mingcv/dsrnet"
] | In the paper 'Single Image Reflection Separation via Component Synergy', what PSNR score did the DSRNet model get on the SIR^2(Objects) dataset
| 26.28 | Title: Single Image Reflection Separation via Component Synergy
Abstract: AbstractThe reflection superposition phenomenon is complex and widely distributed in the real world, which derives various simplified linear and nonlinear formulations of the problem. In this paper, based on the investigation of the weaknesses o... | 2023 | public |
CATT | CBHG | CATT: Character-based Arabic Tashkeel Transformer | 2024-07-03T00:00:00 | https://arxiv.org/abs/2407.03236v3 | [
"https://github.com/abjadai/catt"
] | In the paper 'CATT: Character-based Arabic Tashkeel Transformer', what DER(%) score did the CBHG model get on the CATT dataset
| 10.808 | Title: CATT: Character-based Arabic Tashkeel Transformer
Abstract: AbstractTashkeel, or Arabic Text Diacritization (ATD), greatly enhances the comprehension of Arabic text by removing ambiguity and minimizing the risk of misinterpretations caused by its absence. It plays a crucial role in improving Arabic text process... | 2024-2025 | public |
THUMOS’14 | ActionMamba(InternVideo2-6B) | Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding | 2024-03-14T00:00:00 | https://arxiv.org/abs/2403.09626v1 | [
"https://github.com/opengvlab/video-mamba-suite"
] | In the paper 'Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding', what mAP IOU@0.5 score did the ActionMamba(InternVideo2-6B) model get on the THUMOS’14 dataset
| 76.90 | Title: Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Abstract: AbstractUnderstanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly pr... | 2024-2025 | public |
ADE20K-150 | TTD (MaskCLIP) | TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias | 2024-03-30T00:00:00 | https://arxiv.org/abs/2404.00384v2 | [
"https://github.com/shjo-april/TTD"
] | In the paper 'TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias', what mIoU score did the TTD (MaskCLIP) model get on the ADE20K-150 dataset
| 12.7 | Title: TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
Abstract: AbstractWe identify a critical bias in contemporary CLIP-based models, which we denote assingle tag bias. This bias manifests as a disproportionate focus on a singular tag (word) while neglecting other ... | 2024-2025 | public |
ImageNet 512x512 | EDM2-S w/ guidance interval | Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models | 2024-04-11T00:00:00 | https://arxiv.org/abs/2404.07724v2 | [
"https://github.com/kynkaat/guidance-interval"
] | In the paper 'Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models', what FID score did the EDM2-S w/ guidance interval model get on the ImageNet 512x512 dataset
| 1.68 | Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged.◄Feelinglucky?ConversionreportReportan issueView originalon arXiv►CopyrightPrivacy PolicyGenerated on Sun May 5 18:57:54 2024 byLaTeXML
Conversion to HTML had a Fatal error and exited abruptly. This document may be tr... | 2024-2025 | public |
YouTube-UGC | ReLaX-VQA | ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment | 2024-07-16T00:00:00 | https://arxiv.org/abs/2407.11496v1 | [
"https://github.com/xinyiw915/relax-vqa"
] | In the paper 'ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment', what PLCC score did the ReLaX-VQA model get on the YouTube-UGC dataset
| 0.8204 | Title: ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
Abstract: AbstractWith the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consum... | 2024-2025 | public |
ChEBI-20 | BioT5 | BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations | 2023-10-11T00:00:00 | https://arxiv.org/abs/2310.07276v3 | [
"https://github.com/QizhiPei/BioT5"
] | In the paper 'BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations', what BLEU-2 score did the BioT5 model get on the ChEBI-20 dataset
| 63.5 | Title: BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Abstract: AbstractRecent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limi... | 2023 | public |
COCO-SP | NeuralWalker | Learning Long Range Dependencies on Graphs via Random Walks | 2024-06-05T00:00:00 | https://arxiv.org/abs/2406.03386v2 | [
"https://github.com/borgwardtlab/neuralwalker"
] | In the paper 'Learning Long Range Dependencies on Graphs via Random Walks', what macro F1 score did the NeuralWalker model get on the COCO-SP dataset
| 0.4398 ± 0.0033 | Title: Learning Long Range Dependencies on Graphsvia Random Walks
Abstract: AbstractMessage-passing graph neural networks (GNNs), while excelling at capturing local relationships, often struggle with long-range dependencies on graphs. Conversely, graph transformers (GTs) enable information exchange between all nodes b... | 2024-2025 | public |
ActivityNet-QA | MA-LMM | MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding | 2024-04-08T00:00:00 | https://arxiv.org/abs/2404.05726v2 | [
"https://github.com/boheumd/MA-LMM"
] | In the paper 'MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding', what Accuracy score did the MA-LMM model get on the ActivityNet-QA dataset
| 49.8 | Title: MA-LMM: Memory-Augmented Large Multimodal Modelfor Long-Term Video Understanding
Abstract: AbstractWith the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimo... | 2024-2025 | public |
Weather (192) | PRformer | PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.10483v1 | [
"https://github.com/usualheart/prformer"
] | In the paper 'PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting', what MSE score did the PRformer model get on the Weather (192) dataset
| 0.188 | Title: PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting
Abstract: AbstractThe self-attention mechanism in Transformer architecture, invariant to sequence order, necessitates positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positi... | 2024-2025 | public |
Electricity (336) | PRformer | PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.10483v1 | [
"https://github.com/usualheart/prformer"
] | In the paper 'PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting', what MSE score did the PRformer model get on the Electricity (336) dataset
| 0.161 | Title: PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting
Abstract: AbstractThe self-attention mechanism in Transformer architecture, invariant to sequence order, necessitates positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positi... | 2024-2025 | public |
miniF2F-test | COPRA + GPT-4-turbo | An In-Context Learning Agent for Formal Theorem-Proving | 2023-10-06T00:00:00 | https://arxiv.org/abs/2310.04353v5 | [
"https://github.com/trishullab/copra"
] | In the paper 'An In-Context Learning Agent for Formal Theorem-Proving', what Pass@1 score did the COPRA + GPT-4-turbo model get on the miniF2F-test dataset
| 30.7 | Title: An In-Context Learning Agent for Formal Theorem-Proving
Abstract: AbstractWe present an in-context learning agent for
formal theorem-proving in environments like Lean and Coq. Current state-of-the-art models for the problem are finetuned on environment-specific proof data. By contrast, our approach, calledCopra... | 2023 | public |
Youtube-VIS 2022 Validation | CTVIS (Swin-L) | CTVIS: Consistent Training for Online Video Instance Segmentation | 2023-07-24T00:00:00 | https://arxiv.org/abs/2307.12616v1 | [
"https://github.com/kainingying/ctvis"
] | In the paper 'CTVIS: Consistent Training for Online Video Instance Segmentation', what mAP_L score did the CTVIS (Swin-L) model get on the Youtube-VIS 2022 Validation dataset
| 46.4 | Title: CTVIS: Consistent Training for Online Video Instance Segmentation
Abstract: AbstractThe discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is directly supervised by the contrastive loss computed ... | 2023 | public |
ChartQA | PaLI-3 (w/ OCR) | PaLI-3 Vision Language Models: Smaller, Faster, Stronger | 2023-10-13T00:00:00 | https://arxiv.org/abs/2310.09199v2 | [
"https://github.com/kyegomez/PALI3"
] | In the paper 'PaLI-3 Vision Language Models: Smaller, Faster, Stronger', what 1:1 Accuracy score did the PaLI-3 (w/ OCR) model get on the ChartQA dataset
| 69.5 | Title: PaLI-3 Vision Language Models:Smaller, Faster, Stronger
Abstract: AbstractThis paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) mod... | 2023 | public |
Set14 - 4x upscaling | DUKD | Data Upcycling Knowledge Distillation for Image Super-Resolution | 2023-09-25T00:00:00 | https://arxiv.org/abs/2309.14162v4 | [
"https://github.com/yun224/dukd"
] | In the paper 'Data Upcycling Knowledge Distillation for Image Super-Resolution', what PSNR score did the DUKD model get on the Set14 - 4x upscaling dataset
| 28.80 | Title: Data Upcycling Knowledge Distillation for Image Super-Resolution
Abstract: AbstractKnowledge distillation (KD) emerges as a promising yet challenging technique for compressing deep neural networks, aiming to transfer extensive learning representations from proficient and computationally intensive teacher models... | 2023 | public |
ScanNet200 | PonderV2 + SparseUNet | PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08586v3 | [
"https://github.com/OpenGVLab/PonderV2"
] | In the paper 'PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm', what val mIoU score did the PonderV2 + SparseUNet model get on the ScanNet200 dataset
| 32.3 | Title: PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Abstract: AbstractIn contrast to numerous NLP and 2D computer vision foundational models, the learning of a robust and highly generalized 3D foundational model poses considerably greater challenges. This is primarily due to th... | 2023 | public |
COD | ZoomNeXt-PVTv2-B4 | ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection | 2023-10-31T00:00:00 | https://arxiv.org/abs/2310.20208v4 | [
"https://github.com/lartpang/zoomnext"
] | In the paper 'ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection', what MAE score did the ZoomNeXt-PVTv2-B4 model get on the COD dataset
| 0.017 | Title: ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection
Abstract: AbstractRecent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic sim... | 2023 | public |
AgNews | vONTSS | vONTSS: vMF based semi-supervised neural topic modeling with optimal transport | 2023-07-03T00:00:00 | https://arxiv.org/abs/2307.01226v2 | [
"https://github.com/xuweijieshuai/vONTSS"
] | In the paper 'vONTSS: vMF based semi-supervised neural topic modeling with optimal transport', what C_v score did the vONTSS model get on the AgNews dataset
| 0.49 | Title: vONTSS: vMF based semi-supervised neural topic
modelingwith optimal transport
Abstract: AbstractRecently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorpo... | 2023 | public |
Refer-YouTube-VOS (2021 public validation) | GLEE-Pro | General Object Foundation Model for Images and Videos at Scale | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.09158v1 | [
"https://github.com/FoundationVision/GLEE"
] | In the paper 'General Object Foundation Model for Images and Videos at Scale', what J&F score did the GLEE-Pro model get on the Refer-YouTube-VOS (2021 public validation) dataset
| 70.6 | Title: General Object Foundation Model for Images and Videos at Scale
Abstract: Abstract**footnotetext:Equal Technical Contribution.†Correspondence to Xiang Bai<<xbai@hust.edu.cn>>and Song Bai<<songbai.site@gmail.com>>.We present GLEE in this work, an object-level foundation model for locating and identifying objects ... | 2023 | public |
Amazon-Electronics | HetroFair | Heterophily-Aware Fair Recommendation using Graph Convolutional Networks | 2024-01-31T00:00:00 | https://arxiv.org/abs/2402.03365v2 | [
"https://github.com/nematgh/hetrofair"
] | In the paper 'Heterophily-Aware Fair Recommendation using Graph Convolutional Networks', what NDCG@20 score did the HetroFair model get on the Amazon-Electronics dataset
| 0.0525 | Title: Heterophily-Aware Fair Recommendation using Graph Convolutional Networks
Abstract: AbstractIn recent years, graph neural networks (GNNs) have become a popular tool to improve the accuracy and performance of recommender systems. Modern recommender systems are not only designed to serve the end users, but also to... | 2024-2025 | public |
Office-Home | PromptStyler (CLIP, ResNet-50) | PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization | 2023-07-27T00:00:00 | https://arxiv.org/abs/2307.15199v2 | [
"https://github.com/zhanghr2001/promptta"
] | In the paper 'PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization', what Average Accuracy score did the PromptStyler (CLIP, ResNet-50) model get on the Office-Home dataset
| 73.6 | Title: PromptStyler: Prompt-driven Style Generationfor Source-free Domain Generalization
Abstract: AbstractIn a joint vision-language space, a text feature (e.g., from “a photo of a dog”) could effectively represent its relevant image features (e.g., from dog photos). Also, a recent study has demonstrated the cross-mo... | 2023 | public |
ImageNet | VkD (T:RegNety 160 S:DeiT-S) | $V_kD:$ Improving Knowledge Distillation using Orthogonal Projections | 2024-03-10T00:00:00 | https://arxiv.org/abs/2403.06213v1 | [
"https://github.com/roymiles/vkd"
] | In the paper '$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections', what Top-1 accuracy % score did the VkD (T:RegNety 160 S:DeiT-S) model get on the ImageNet dataset
| 82.9 | Title: VkD::subscript𝑉𝑘𝐷absentV_{k}D:Improving Knowledge Distillation using Orthogonal Projections
Abstract: AbstractKnowledge distillation is an effective method for training small and efficient deep learning models. However, the efficacy of a single method can degenerate when transferring to other tasks, modalit... | 2024-2025 | public |
Segmentation in the Wild | HIPIE | Hierarchical Open-vocabulary Universal Image Segmentation | 2023-07-03T00:00:00 | https://arxiv.org/abs/2307.00764v2 | [
"https://github.com/berkeley-hipie/hipie"
] | In the paper 'Hierarchical Open-vocabulary Universal Image Segmentation', what Mean AP score did the HIPIE model get on the Segmentation in the Wild dataset
| 41.6 | Title: Hierarchical Open-vocabulary Universal Image Segmentation
Abstract: AbstractOpen-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple level... | 2023 | public |
OVIS validation | DVIS(Swin-L, Offline) | DVIS: Decoupled Video Instance Segmentation Framework | 2023-06-06T00:00:00 | https://arxiv.org/abs/2306.03413v3 | [
"https://github.com/zhang-tao-whu/DVIS"
] | In the paper 'DVIS: Decoupled Video Instance Segmentation Framework', what mask AP score did the DVIS(Swin-L, Offline) model get on the OVIS validation dataset
| 49.9 | Title: DVIS: Decoupled Video Instance Segmentation Framework
Abstract: AbstractVideo instance segmentation (VIS) is a critical task with diverse applications, including autonomous driving and video editing. Existing methods often underperform on complex and long videos in real world, primarily due to two factors. Firs... | 2023 | public |
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