# Dynamic Template Initialization for Part-Aware Person Re-ID

Kalana Abeywardena  Shechem Sumanthiran  Sanoojan Baliah  Nadarasar Bahavan
Nalith Udugampola  Ajith Pasqual  Chamira Edussooriya  Ranga Rodrigo
Department of Electronic and Telecommunication Engineering
University of Moratuwa, Sri Lanka
<EMAIL_ADDRESS>
###### Abstract

Many of the existing Person Re-identification (Re-ID) approaches depend on
feature maps which are either partitioned to localize parts of a person or
reduced to create a global representation. While part localization has shown
significant success, it uses either naïve position-based partitions or static
feature templates. These, however, hypothesize the pre-existence of the parts
in a given image or their positions, ignoring the input image-specific
information which limits their usability in challenging scenarios such as Re-
ID with partial occlusions and partial probe images. In this paper, we
introduce a spatial attention-based Dynamic Part Template Initialization
module that dynamically generates part-templates using mid-level semantic
features at the earlier layers of the backbone. Following a self-attention
layer, human part-level features of the backbone are used to extract the
templates of diverse human body parts using a simplified cross-attention
scheme which will then be used to identify and collate representations of
various human parts from semantically rich features, increasing the
discriminative ability of the entire model. We further explore adaptive
weighting of part descriptors to quantify the absence or occlusion of local
attributes, and suppress the contribution of the corresponding part
descriptors to the matching criteria. Extensive experiments on holistic,
occluded, and partial Re-ID task benchmarks demonstrate that our proposed
architecture is able to achieve competitive performance. Codes will be
included in the supplementary material and will be made publicly available.

##  1 Introduction

Person Re-identification (Re-ID) has fast become one of the most critical
vision tasks in the surveillance domain, especially due to its mission-
critical application in video surveillance, autonomous driving, and activity
analysis [49, 58, 47, 24]. Re-ID is a task in which a probe image is used to
search for a person among a gallery of person images cropped from a larger
scene [6, 56]. Although Re-ID performance has recently improved, certain
inherent challenges still require solutions, especially when dealing with
persons being occluded in cluttered scenes.

Humans utilize different visual cues to Re-ID in different contexts: for quick
re-identification, humans utilize vivid global features that include the color
of clothing and large accessories associated with the person; whereas in more
challenging environments, people pay attention to discriminating and unique
local features such as the features of a face [44]. This illustrates that
awareness of local features unique to a person can be beneficial in successful
Re-ID. However, designing algorithms that are aware of local features can be
challenging as they need to learn subtle changes to discriminate successfully
among different persons.

In recent works, many solutions have been proposed to learn discriminative
local features. These can be generally divided into three categories: (1)
hand-crafted splitting-based methods that divide and extract part features
from the local patches [9, 59, 13] and stripes [4, 39, 33] of the image or the
feature map; (2) extra semantic-based methods that directly utilize either
human parsing [14, 12] or pose estimation models [29, 41, 5]; and (3)
attention-based methods [38, 62] that use attention schemes to localize
discriminative human parts. However, each of these methods has inherent flaws.
Hand-crafted splitting methods introduce additional background noise and poor
alignment of the human parts due to the coarse features extracted. Human
pose/parsing methods heavily rely on the performance of the off-the-shelf
human pose/parsing estimation models, which are trained on datasets of
different domains. Further, the adoption of human part detectors in Re-ID is
challenging due to the lack of extra annotations in Person Re-ID datasets.
Attention schemes utilize static templates that are separately learnt during
training to localize and identify which portions of the feature maps
correspond to human body parts. Finally, the above methods strongly assume
that every body part is present in every image, disregarding the possibility
of occlusions or partial images.

To address these flaws, we introduce a mechanism that learns part-based
information using only the identity labels. Unlike the recent work that adopts
either naïve position-based partitioning [12, 41] or static feature templates
[4, 39, 22], we propose a method which dynamically initialize the part-aware
spatial templates using input image-specific features extracted from the
backbone model. We model the correlation between the extracted mid-level local
features from the earlier layers of a backbone network that represents human
part-level features and a set of learnable part-weights in our proposed
_Dynamic Part Template Initialization (DPTI) module_. The acquired feature
maps from the backbone model are first passed through a spatial self-attention
layer to suppress background noise. The resulting attentive features are used
to dynamically initialize part-aware templates. The proposed dynamic template
generation enables the model to utilize mid-level features of the backbone as
auxiliary information and fully utilize the power of attention mechanism to
obtain human part-templates. Unlike the static templates in [22], where the
learnt kernels are utilized to extract human part templates, our proposed
dynamic templates do not make assumptions about the presence, location, or
size of any body parts which enhances its capability to accurately identify
the useful attributes from a given input image.

By initializing part-templates dynamically, we do not rely on the extra
annotations of each human body part. Instead, the templates are used to
localize and aggregate discriminative features which relate to human parts
from the high-level features of the last layer of the backbone using our
proposed _Part Attention Module_. This is achieved via a cross-attention
scheme, resulting in the final part-descriptors used for matching persons. We
are able to fully leverage the discriminative power of the cross-attention by
using input-aware queries, as used in the transformer architectures for image
classification [20]. Therefore, the most prominent local features for a given
pedestrian image will be extracted and converted into an embedding without
manually altering the number of templates to be extracted based on the task as
with static part-templates. To ensure every part embedding does not collapse
to the same vector, we propose a modified diversity loss function from [22].
This encourages each template to learn different discriminative features about
a pedestrian. Dynamic part-template initialization is useful for dealing with
a number of challenges that are present not only in the Re-ID domain but in
other applications as well.

We further explore an _Adaptive Part Weighting_ mechanism that weighs the
part-descriptors using the part-template attention weights. We hypothesize the
lower mean attention activation throughout the intermediate feature map
indicates whether the part being localized is either absent or occluded. Thus,
we quantitatively analyzed how it affects the person Re-ID, specially with
partial probes and occluded images, by re-scaling the part-descriptors based
on the mean activation of the part-template initialization module. We use the
cosine distance metric with normalization of the feature vectors to ensure
that the adaptive weighting reduces the impact of occluded or missing parts on
the final distance.

The major contributions of our method can be summarized as follows:

  1. 1.

We propose a dynamic part-based spatial template initialization mechanism from
intermediate feature maps of the backbone network to encourage template
initialization based on less discriminative features.

  2. 2.

We propose to use a cross-attention mechanism between the dynamic templates
and high-level discriminative feature maps to collate discriminative
information of parts.

  3. 3.

We explore an adaptive weighting mechanism in order to deal with occlusions
and omissions of parts in the person images.

##  2 Related Work

Person Re-Identification: Person Re-ID aims to match complete person images
across disjoint camera views [54, 3, 21, 1] assuming all the human parts are
visible in a given person image. Due to large intra-class and small inter-
class variations caused by different viewpoints, poses illuminations, and
occlusions, Re-ID is a challenging task. Existing methods for Re-ID can be
grouped into hand-crafted descriptors [24, 28], metric learning methods [19,
45], and deep representation learning [39, 25, 35, 37, 36, 18, 21, 23]. Many
recent state-of-the-art works utilize part-based features for holistic person
Re-ID task using different approaches: human parsing models and assembling
final discriminative representations with part-level features [18]; uniform
partitioning of feature maps to learn part-level features by multiple
classifiers [39, 33]; and attention-based methods to extract part-level
features [53, 22]. Building on attention-based part-aware models, we propose a
DPTI module that creates local feature-aware templates in a self-supervised
manner without any additional annotations or inputs to localize human parts
within a given input-image. Further, our part templates are adaptive, rather
than having static partitions of the input image or the outputs of the feature
extractor.

Figure 1: The proposed architecture: Intermediate feature maps are used by the
DPTI module to compute part-templates. Feature maps from the final layer of
the backbone are passed through a self-attention layer. The resulting
attention outputs are collated by the Part Attention module to localize
discriminative features of human parts. These are then weighted and combined
with the global descriptor to form the final descriptor.

Occluded Person Re-ID: Given occluded probe images, occluded person Re-ID aims
to find the same person of full-body or occluded appearance in disjoint
cameras. This task is more challenging due to incomplete information and
spatial misalignment. To deal with these challenges, some works have combined
occluded/non-occluded binary classification loss [61] to distinguish the
occluded images from holistic ones. Others reconstruct occluded feature maps
and propose a spatial background-foreground classifier to reduce background
clutter [9]. Pose-guided feature alignment [30] uses pose landmarks to mine
discriminative parts and address occlusion noise. A pose-guided visible part
matching model has been proposed to learn discriminative part features with
pose-guided attention [5]. In [41], the authors exploit graph convolutional
layers to learn high order human part relations for robust alignment. Others
transform the holistic image to align with the partial one and then calculate
the distance of the aligned pairs [27]. Recently, [22] proposed part
prototype-based transformer decoder to learn the part templates to identify
the pedestrian parts.

Although the above methods can solve the occlusion problem to some extent,
most of them heavily rely on off-the-shelf human parsing models or pose
estimators. With our proposed method, we model the prominence of local
features that are initialized as templates. Therefore, the occluded parts of a
gallery image will be suppressed by the part-template initialization module,
and have a low impact on Re-ID.

Partial Person Re-ID: Partial person images can occur due to imperfect
detection of persons. Partial person Re-ID aims to match partial probe images
to holistic gallery images. Attempts at addressing partial Re-ID include a
global-to-local matching model to provide complementary spatial layout
information [59]. An alignment-free approach that exploits the reconstruction
error to reconstruct the feature map of a partial query from the holistic
pedestrian was proposed in [9], and further improved by a foreground-
background mask to avoid the influence of backgrounds clutter in [14]. STNReID
that combines a spatial transformer network and a Re-ID network for partial
Re-ID, was introduced in [27]. In [38], a Visibility-aware Part Model was
introduced to perceive the visibility of part regions through self-
supervision. In [15], a self-supervised learning framework that learns
correspondence between image patches without any additional part-level
supervision was proposed to recognize the partial input with the assistance of
a part-part correspondence learning. A part-aware transformer network with a
prototype transformer decoder that learns diversified part-based
representation was proposed by [22].

Our proposed approach is able to deal with partial images by dynamically
initialize templates and weigh the corresponding parts based on the partial
input image. Therefore, the architecture will only attempt to match, what is
present and ignore the sections of the image that are absent.

##  3 Proposed Architecture

###  3.1 Overview

Our architecture is shown in Fig. 1. There are 4 components of significance:
feature extractor, DPTI module, part attention module and adaptive weighting
module.

The feature extractor used is a generic convolutional neural network (CNN). A
person image is input into the feature extractor, which reduces the spatial
dimensions and increases the channel dimension as the image propagates through
the network. The DPTI module uses the feature maps extracted from part-way
through the feature extractor, such as from the last layer of the penultimate
stage, which represent the mid-level semantics of local attributes of person
from the input image. The part templates are initialized from these feature
maps using a cross-attention [40] mechanism. The part attention module takes
the part templates as inputs, along with the feature maps from the final layer
of the feature extractor. The feature maps are fed through a self-attention
[40] encoder, and then a cross-attention network with the part templates is
used as the queries. The outputs form the part descriptors of the person. The
global descriptor and part descriptors are concatenated to form the final
person descriptor. During training, this concatenated descriptor is jointly
optimized using cross-entropy loss and triplet loss, along with a modified
diversity loss to encourage differential part learning. The identity
predictions are made using the descriptor, using a cosine distance metric to
determine similarity between descriptors.

###  3.2 Feature Extractor

While any generic feature extractor can be used for the feature extractor, we
utilize the popular ResNet-50 [8]. To obtain larger feature maps at the final
layer and to be consistent with previous works, we change the stride of the
last layer from 2 to 1. At the input, each person image is resized to a fixed
size of $H\times W$. The ResNet-50 architecture can be divided into 4 stages,
each stage working on progressively smaller feature maps. The output feature
maps of the final layer of the $l$-th stage is
$\mathbf{F}_{l}\in\mathbb{R}^{n_{l}\times h_{l}\times w_{l}}$, where $n_{l}$
denotes the number of channels, $h_{l}$ is the height and $w_{l}$ is the
width. We choose the stage $l<4$ to be one of the first 3 stages. Similarly,
the output feature maps of the final layer of the final stage is
$\mathbf{F}\in\mathbb{R}^{n\times h\times w}$.

###  3.3 Dynamic Part Template Initialization Module

Features extracted by the earlier layers of deep CNNs tend to be more generic
and have lower-level semantics. These are generally not very discriminative
and are more useful for later layers to localize more complex, discriminative
features. Therefore, instead of using $\mathbf{F}$ to initialize the part
templates, we choose feature maps $\mathbf{F}_{l}$.

To create the templates, we first pass $\mathbf{F}_{l}$ through a $1\times 1$
convolutional layer to reduce the feature dimensions to $d$. We obtain
$\tilde{\mathbf{F}}_{l}\in\mathbb{R}^{d\times h_{l}\times w_{l}}$, where
$d<n_{l}$, and pass it through a spatial self-attention encoder to suppress
background noise in the feature maps. We reshape $\tilde{\mathbf{F}}_{l}$ to
$d\times h_{l}w_{l}$ to treat each spatial location as a separate feature
vector, such that
$\tilde{\mathbf{F}}_{l}=[\bm{f}_{l,1},\bm{f}_{l,2},\dots,\bm{f}_{l,h_{l}w_{l}}]$,
where $\bm{f}_{l,i}\in\mathbb{R}^{d\times 1}$ for $i=1,\dots,h_{l}w_{l}$ is
the feature vector of the $i$-th location. Then, the features are linearly
projected to obtain the keys, queries and values as given by

$\displaystyle\bm{q}_{l,i}$ $\displaystyle=\mathbf{W}_{l,q}\bm{f}_{l,i},$
$\displaystyle\mathbf{K}_{l}$
$\displaystyle=\mathbf{W}_{l,k}\tilde{\mathbf{F}}_{l},$
$\displaystyle\mathbf{V}_{l}$
$\displaystyle=\mathbf{W}_{l,v}\tilde{\mathbf{F}}_{l},$ (1)

where
$\mathbf{W}_{l,k},\mathbf{W}_{l,q},\mathbf{W}_{l,v}\in\mathbb{R}^{d\times d}$
matrices are the linear projections for the keys, queries, and values
respectively. The attention outputs are subsequently calculated as

$\displaystyle\bm{\beta}_{l,i}$
$\displaystyle=\mathrm{softmax}\left(\frac{\mathbf{K}_{l}^{T}\bm{q}_{l,i}}{\sqrt{d_{k}}}\right),$
(2) $\displaystyle\bm{g}_{l,i}$
$\displaystyle=\mathbf{V}_{l}~{}\bm{\beta}_{l,i},$ (3)

where $d_{k}$ is the scaling factor. The final attention outputs are given as

$\bm{a}_{l,i}=\mathbf{FFN}(\bm{g}_{l,i}+\bm{f}_{l,i})+\bm{g}_{l,i},$ (4)

where $\mathbf{FFN}(\cdot)$ is a feed-forward network of equal input and
output dimensions. The matrix of attention outputs from the $l$-th
intermediate layer is denoted as
$\mathbf{A}_{l}=[\bm{a}_{l,1},\dots,\bm{a}_{l,h_{l}w_{l}}]$.

We initialize $N$ part templates for each input. The part templates are
initialized by a simplified cross-attention mechanism, in which the learnt
keys are represented by the linear projection matrix. Therefore, the part-
templates, denoted as
$\mathbf{T}_{t}=[\bm{t}_{1},\dots,\bm{t}_{N}]\in\mathbb{R}^{d\times N}$, are
obtained by

$\displaystyle\bm{\gamma}_{i}$ $\displaystyle=\mathbf{A}_{l}^{T}\bm{q}_{t,i},$
(5) $\displaystyle\bm{t}_{i}$
$\displaystyle=\mathbf{W}_{t,v}\mathbf{A}_{l}\mathrm{softmax}\left(\bm{\gamma}_{i}\right),$
(6)

where $\bm{q}_{t,i}\in\mathbb{R}^{d\times 1},i=1,\dots,N$ represent the learnt
part-weights that serve as the queries.
$\mathbf{W}_{t,v}\in\mathbb{R}^{d\times d}$ is the linear projection matrix
for the template values.

The part templates $\bm{t}_{i}$ are therefore created from the input data
itself, and are able to capture data-specific templates of various human parts
that are later used to search for and aggregate discriminative human part
features. Our proposed scheme, as illustrated in Fig. 2, reduces the
complexity of doing multiple linear projections of keys and queries. It also
allows for faster and easier optimization of the part-weights $\bm{q}_{t,i}$.

###  3.4 Part Attention Module

The Part Attention Module is required to localize and aggregate discriminative
information about each part of the person identified by the templates
$\bm{t}_{i}$, $i=1,\dots,N$. To maximize discriminative power of the model, we
use the feature maps from the final layer $\mathbf{F}$ to create the final
part vectors. Similar to the template creation, we first pass $\mathbf{F}$
through a $1\times 1$ convolutional layer to reduce the feature dimensions
from $n$ to $d$ and obtain $\tilde{\mathbf{F}}$. Then, following equations
(1), (2), (3), and (4), we pass $\tilde{\mathbf{F}}$ through a spatial encoder
layer and obtain the feature vectors, which are defined as $\mathbf{F}_{a}$.

The obtained feature vectors are then used as the keys and values, while the
part templates are used as the queries of a cross-attention encoder layer.
Specifically, the keys, queries, and values are obtained by linear projection
of the inputs as given by

$\displaystyle\bm{q}_{i}$ $\displaystyle=\mathbf{W}_{q}\bm{f}_{t,i},$
$\displaystyle\mathbf{K}$ $\displaystyle=\mathbf{W}_{k}\mathbf{F}_{a},$
$\displaystyle\mathbf{V}$ $\displaystyle=\mathbf{W}_{v}\mathbf{F}_{a},$ (7)

where $\mathbf{W}_{q},\mathbf{W}_{k},\mathbf{W}_{v}\in\mathbb{R}^{d\times d}$
are the linear projection matrices. The attention outputs are obtained
following equations (2) and (3), and the final part vectors are obtained after
they are passed through a feed-forward network, as in equation (4). The final
output of this is $N$ part vectors $\bm{f}_{p,i}$, $i=1,\dots,N$.

The global descriptor vector is obtained by applying global average pooling
(GAP) over all outputs of the spatial encoder layer,
$\bm{f}_{a,i},i=1,\dots,hw$.

Finally, all $N$ part vectors and the global descriptor vector are
concatenated to form the final descriptor vector
$\bm{f}\in\mathbb{R}^{(N+1)d\times 1}$.

Figure 2: DPTI module: The features are correlated with learnt part-weights to
collate the attention vectors and initialize the part-templates.

###  3.5 Adaptive Part Weighting

Occluded Re-ID and Partial Re-ID both violate the assumption that all
localized parts of the human body will be present in the person image.
Dynamically initialized part templates will provide some robustness when all
parts are not present. To further strengthen robustness in the presence of
occlusions, we explore adaptive weighting of the part vectors, $\bm{f}_{p,i}$
before the final concatenation. In particular, we obtain the attention weights
that are computed during the template initialization phase in Eq. (5),
$\mathbf{A}=[\bm{\gamma}_{1},\dots,\bm{\gamma}_{N}]\in\mathbb{R}^{h_{l}w_{l}\times
N}$. The weight matrix $\mathbf{A}$ represents the correlation between the
features obtained from the intermediate layer, and the part-weights. The mean
magnitude of each of $\bm{a}_{i}$ represent the level of activation for the
$i$-th part, indicating whether the part is present or not. To leverage this
information, we compute the mean of each weight vector $\bm{\gamma}_{i}$ as
$\bar{\gamma}_{i}$. We apply a softmax operation over all $N$ mean weights,
resulting in a part-presence vector $\bm{\rho}=[\rho_{1},\dots,\rho_{N}]$.
Scaling $\bm{\rho}$ by a factor of $N$, we obtain an adaptive weight for each
of the parts. Then, before concatenating the part descriptors, we scale each
part descriptor by its corresponding weight, as

$\tilde{\bm{f}}_{p,i}=N\rho_{i}\bm{f}_{p,i}~{}.$ (8)

For lower values of $\rho_{i}$ to have lower impact on the final distance
between descriptors, we use the cosine distance metric after normalization.

###  3.6 Loss Function

We apply a weighted combination of cross-entropy loss $\mathcal{L}_{class}$,
triplet loss $\mathcal{L}_{tri}$, and a modified diversity loss [22]
$\mathcal{L}_{div}$. Most works that use part descriptors apply the weighted
loss separately to each of the part descriptors and the global descriptor.
However, we find that concatenating all descriptors into a single descriptor
and jointly optimizing using identity classification loss and triplet loss
yields superior results. The loss function can be written as

$\mathcal{L}=\alpha\mathcal{L}_{class}+\beta\mathcal{L}_{tri}+\gamma\mathcal{L}_{div},$
(9)

where $\alpha,\beta$ and $\gamma$ are the weighting factors of each of the
losses.

Modified Diversity Loss: The diversity loss that we propose can be written as:

$\mathcal{L}_{div}=\frac{1}{N(N-1)}\sum_{i=1}^{N}\sum_{\begin{subarray}{c}j=1\\\
j\neq
i\end{subarray}}^{N}\frac{\langle\bm{f}_{p,i},\bm{f}_{p,j}\rangle^{2}}{||\bm{f}_{p,i}||^{2}_{2}||\bm{f}_{p,j}||^{2}_{2}}~{}.$
(10)

We square each inner product to coerce the part-vectors to be orthogonal, as
opposed to its original form presented in [22] that allows the vectors to
negatively correlated. Negative correlation still implies linear dependence,
leading to part vectors which may not be independent.

##  4 Experiments

###  4.1 Datasets and Evaluation Metrics

To demonstrate the effectiveness of our method, we conduct extensive
experiments on one occluded dataset: Occluded REID [61], two partial Re-ID
datasets: Partial-REID [59] and Partial-iLIDS [57], and one holistic Re-ID
dataset: Market-1501 [55]. The details are as follows.
Occluded-REID [61] is an occluded person dataset captured by mobile cameras,
including 2,000 images belonging to 200 identities. Each identity has five
full-body person images and five occluded person images with different
viewpoints and different types of severe occlusions.
Partial-REID [59] is a specially designed partial person Re-ID benchmark that
includes 600 images from 60 people, with five full-body images in gallery set
and five partial images in query set per person.
Partial-iLIDS [10] is a partial person Re-ID dataset based on the iLIDS
dataset [57] and contains a total of 238 images from 119 people captured by
multiple cameras in the airport, and their occluded regions are manually
cropped.
Market-1501 [55] consists of 1,501 identities captured by 6 cameras. The
training set consists of 12,936 images of 751 identities, the query set
consists of 3,368 images, and the gallery set consists of 19,732 images.
Evaluation Metrics: We adopt standard metrics as in most person Re-ID
literature, namely Rank-1 accuracy and mean average precision (mAP), to
evaluate the quality of different Re-ID models. Additionally, for our own
comparisons in Sec. 4.4, we report Rank-5 accuracy, and also use the mean
Inverse Negative Penalty (mINP) metric introduced in [48] to provide a
comprehensive overview of the performance of the models.

###  4.2 Implementation Details

We conduct all experiments using PyTorch [32] with a single 16GB NVIDIA Quadro
RTX 5000 GPU. Our architecture is implemented on top of the FastReID toolbox
[11]. All pedestrian images are resized to a resolution of $256\times 128$. We
use horizontal flipping, random erasing, padding, and Augmix [16] as data
augmentation. All models are trained for 180 epochs on the Market1501 dataset,
using Adam optimizer with the base learning rate set to $3.5\times 10^{-4}$.
We adopt linear warm-up for the first 10 epochs, increasing the learning rate
from $3.5\times 10^{-5}$. We decay the learning rate by a factor of 10 at
epochs 40, 90, and 150. During training, all feed-forward layers use dropout
with probability 0.1, and all attention modules use dropout with probability
0.2. For occluded and partial datasets, random cropping is also utilized to
prevent overfitting. None of the models compared with state of the art results
include the adaptive weighting module.

For all datasets, the number of part vectors $N$ is set to 8. The attention
modules are all implemented as multi-headed attention with 8 heads. The
embedding dimension $d$ is set to 256. We use a ResNet50 model pretrained on
ImageNet, with the last stride set to 1, and choose the intermediate layer $l$
as 3 to utilize the mid-level features that represents human part-level
information.

###  4.3 Comparison with the state-of-the-art

Method | Rank-1 | mAP
---|---|---
PCB [39] | 92.3 | 77.4
BOT [26] | 94.1 | 85.7
MGN [42] | 95.7 | 86.9
VPM [38] | 93.0 | 80.8
IANet [17] | 94.4 | 83.1
SPReID [18] | 92.5 | 81.3
222indicates that code is not publicly available and results could not be reproduced.DSA-reID [50] | 95.7 | 87.6
PGFA [30] | 91.2 | 76.8
HOReID [41] | 94.2 | 84.9
RGA-SC [51] | 96.1 | 88.4
$\dagger$ EXAM [33] | 95.1 | 85.9
$\dagger$ PAT [22] | 95.4 | 88.0
$\dagger$ HLGAT [52] | 97.5 | 93.4
OURS | 94.9 | 86.3
Table 1: Comparison with state of the art methods on Market1501 dataset.

Results on Holistic Datasets: The results that we obtain on Market1501 are
presented in Table 1. Our method outperforms many recent attempts, and
compares favourably with other state of the art architectures. However, our
proposed architecture does not require any handcrafting, and therefore
requires minimal intervention by way of network design. It is interesting to
note that both HLGAT [52], which far outperforms all other methods, and RGA-SC
[51] are both complementary to our architecture, and can be implemented in
combination. In general, they are valuable for any part-based architecture.
However, the method proposed in [52] presents significant run-time challenges,
as each unique pair of descriptors must be passed through a Graph Attention
Network to compute the final embedding. In general, our model is able to
achieve very competitive results while making very few assumptions about the
nature of the input data.

Method | Rank-1 | mAP
---|---|---
PCB [39] | 14.3 | 38.9
AMC+SWM [59] | 31.2 | 27.3
DSR [9] | 72.8 | 62.8
FPR [14] | 78.3 | 68.0
PVPM [5] | 70.4 | 61.2
GASM [12] | 74.5 | 65.6
HOReID [41] | 80.3 | 70.2
$\dagger$ PAT [22] | 81.6 | 72.1
SSGR [46] | 78.5 | 72.9
$\dagger$ OAMN [2] | 62.6 | 46.1
OURS | 71.3 | 67.4
Table 2: Comparison with state of the art methods on Occluded ReID dataset.

Results on Occluded Datasets: The results we obtain on Occluded ReID are
presented in Table 2. Our method outperforms several recent work while
providing competitive perform with the rest. Unlike DSR [9] and FPR [14] that
use multiple feature extractors to match the occluded with holistic images,
the proposed dynamic part initialization method learns occlusion aware
features that provides close results to them. Our model is capable of
providing required local features that are needed to learn the topology of a
human unlike HOReID [41] that uses external human parsing model to do so. PAT
[22] achieves state of the art results on the Occluded Re-ID dataset. However,
the static templates are specialized for a given dataset that raises questions
about the model’s ability to generalize to other scenarios. Conversely, our
templates are created from the source pedestrian image itself, leading to less
training bias and greater ability to generalize. Further, unlike [22] that
requires alteration of model templates to be learnt, our proposed approach
provides a unique model that extracts the most prominent attributes for a
given input image using mid-level features.

Method | Partial-ReID |  | Partial-iLIDS
---|---|---|---
Rank-1 | Rank-3 |  | Rank-1 | Rank-3
AMC+SWM [59] | 37.3 | 46.0 |  | 21.0 | 32.8
DSR [9] | 50.7 | 70.0 |  | 58.8 | 67.2
$\dagger$ STNReID [27] | 66.7 | 80.3 |  | 54.6 | 71.3
VPM [38] | 67.7 | 81.9 |  | 65.5 | 74.8
PVPM [5] | 78.3 | 87.7 |  | - | -
HOReID [41] | 85.3 | 91.0 |  | 72.6 | 86.4
$\dagger$ PAT [22] | 88.0 | 92.3 |  | 76.5 | 88.2
$\dagger$ PPCL [15] | 79.0 | 87.3 |  | - | -
OURS | 81.3 | 89.0 |  | 65.6 | 74.8
Table 3: Comparison with state of the art methods on Partial-ReID and Partial-
iLIDS datasets.

Results on Partial Datasets: The results we obtain on Partial ReID are
presented in Table 3. Our proposed architecture outperforms almost every past
work except HOReID [41] and PAT [22] on both the Partial-ReID and Partial-
iLIDS datasets. On Partial-ReID dataset, it is notable that our method
performs competitively well with the state-of-the-art. Unlike [41], [22], we
do not either rely on parsing models and graph-like topologies to match
partial human images or change initialized templates depending on the dataset.
This is further reflected on the results we obtained on Partial-iLIDS.

###  4.4 Ablation

Variation |  | Market-1501 |  | Occluded Re-ID |  | Partial Re-ID
---|---|---|---|---|---|---
|  | R-1 | R-5 | mAP | mINP |  | R-1 | R-5 | mAP | mINP |  | R-1 | R-5 | mAP | mINP
G |  | 93.4 | 97.9 | 82.9 | 53.6 |  | 59.5 | 75.4 | 52.8 | 38.0 |  | 79.0 | 90.3 | 72.4 | 58.8
G + P |  | 92.3 | 97.5 | 79.6 | 43.4 |  | 69.9 | 84.6 | 63.5 | 49.4 |  | 47.3 | 68.7 | 44.9 | 32.9
G + T |  | 94.0 | 98.1 | 87.0 | 62.2 |  | 70.2 | 83.8 | 66.0 | 53.6 |  | 78.0 | 89.7 | 72.3 | 59.3
G + D |  | 95.0 | 98.2 | 86.3 | 60.7 |  | 71.3 | 86.4 | 67.4 | 55.1 |  | 81.3 | 91.7 | 76.2 | 64.2
G + D + W |  | 94.7 | 98.3 | 85.8 | 59.4 |  | 71.9 | 85.7 | 66.3 | 53.1 |  | 81.0 | 90.7 | 76.0 | 63.5
Table 4: Comparison of different components of the proposed architecture on three datasets Backbone | R-1 | R-5 | R-10 | mAP | mINP
---|---|---|---|---|---
Resnet50 | 94.7 | 98.3 | 98.9 | 85.8 | 59.4
ResNet50 with IBN | 94.8 | 98.2 | 99.0 | 87.1 | 63.1
OSNet | 94.4 | 98.3 | 98.2 | 86.7 | 62.3
ResNet18 | 93.1 | 97.9 | 98.9 | 81.7 | 50.2
Table 5: Results of the proposed architecture using various backbones on
Market 1501. Figure 3: Attention maps computed by the template initialization
layer. Images are taken from the Market1501 dataset.

Effect of DPTI:  We perform ablation studies to determine the impact and
effectiveness of each of the components of the proposed architecture. We
compare variations of the proposed DPTI module against a baseline which uses
only the global features obtained through a self-attention layer (G). To prove
the effectiveness of our dynamic initialization, we introduce 2 variations.
The first is to remove the template initialization module, and partition the
attention outputs $\bm{f}_{a_{l},i}$ into a grid of $N$ sections, and average
each section into a single feature vector which is used as the part template
(P). This demonstrates the importance of dynamically localizing parts, instead
of making assumptions about their location in the image. The second is to use
static templates instead of dynamically initialized templates (T). This
demonstrates the importance of data-specific templates. Our proposed model
uses dynamically initialized templates (D). Finally, we also present the
results when the final feature vectors are weighted using the adaptive
weighting scheme (W) introduced in Sec. 3.5. The results are presented in
Table 4. As demonstrated by the results presented in the table, using averaged
features from the intermediate layer (P) alone impacts performance negatively,
especially for partial Re-ID. This is because the model assumes that a given
part only occurs in a given section of the pedestrian image. Using static
templates (T) increases performance. However, without dynamic initialization,
the templates cannot adjust to differences in input data, and will only
reflect the characteristics of the training dataset. As such, the static
template initialization method is able to perform at a similar level for
holistic Re-ID, but performance drops off for Occluded and Partial Re-ID. It
is interesting to note that the adaptive weighting module (W) does not produce
any significant impact on performance, and in fact, slightly degrades
performance for Occluded Re-ID. We include it only as an exploration of
adaptive weighting.

Effect of backbone architecture: We also evaluate our architecture using
different backbones on Market 1501 to demonstrate that our architecture is
able to leverage the feature extraction properties of various different
models. We use a small model, ResNet18, and models of different structures,
such as ResNet50 with IBN [31], and OSNet [60]. The results are presented in
Table 5. Impressively, the ResNet18 model with our proposed architecture is
able to achieve a mINP score of 50.2 %, despite its small size. Additionally,
our architecture is able to adapt well to different backbones, and utilize
their feature extraction capabilities. Both the ResNet50 with IBN and the
OSNetx1.0 achieve similar results to that of the ResNet50, with the ResNet50
with IBN unsurprisingly surpassing the results obtained by the baseline
ResNet50, due to its superior generalization abilities.

Qualitative Results: We plot the attention maps from the template
initialization layer to conduct a qualitative investigation into what the
part-templates are focusing on. The results are plotted in Fig. 3. As shown by
the plot, each template is paying attention to a particular part of the human
body, including the head, torso, shoes, and even various accessories being
carried by the pedestrian. These are generally useful parts to focus on for
Re-ID of a person. It is intuitive that the model would consider these
features when determining what parts to localize.

###  4.5 Limitations and Negative Social Impacts

Results presented by most Person Re-ID architectures are limited by the data
available for training. High mAP and Rank-1 accuracy scores can only be
achieved under the conditions present in the dataset. One significant
limitation of our work is the lack of training datasets. With the termination
of both DukeMTMC-Re-ID [34, 61] due to privacy concerns [7], and the MSMT17
[43] from the original website for unknown reasons, we felt it was best to not
use those datasets. However, we are confident that the principles introduced
are transferable to most applications of Person Re-ID, and many other
applications including vehicle Re-ID and multi-camera tracking. Additionally,
many existing Person Re-ID datasets are too small to successfully train
attention modules.

There are many assumptions associated with closed-world Person Re-ID, such as
well-aligned bounding boxes, sufficient illumination, and a closed gallery
set. These do not hold true in real-world applications. However, these are
limitations of closed-world Person Re-ID, and we do not consider them as
limitations of our contribution.

A serious ethical concern for collecting large Person Re-ID datasets is the
privacy of the human participants, as exemplified by the DukeMTMC-Re-ID
termination. As observed by [7], DukeMTMC-Re-ID continues to be used both for
research and commercial applications. Another issue which is largely ignored
by researchers is the biases that are introduced by these datasets into the
models, which are subsequently used for commercial applications. Deep
investigation into these biases should be encouraged before research commences
on any dataset.

Overall, the Re-ID domain has serious societal implications. Increasing public
surveillance worldwide is driven by the techniques being developed herein.
While there are definite security benefits to intelligent surveillance, it may
also be put to ill-use, or become compromised due to unauthorized access by
malicious parties. Additionally, inaccuracy in Re-ID models could lead to
mistaken identity, which may have severe consequences. Thus, human
verification in the loop is essential, as is moving towards more secure,
accurate models for responsible Person Re-ID.

##  5 Conclusion

In this work, we introduce a novel dynamic part template initialization
module, specially for partial and occluded person Re-ID, to extract part-based
spatial templates by utilizing mid-level features extracted at middle layers
of the backbone. We obtain background noise-reduced attention vectors by
passing the mid-level feature maps through a self-attention layer followed by
our cross-attention-inspired projection to initialize part templates. To
extract the discriminative part-descriptors with only identity labels, we
propose a part attention module and modified diversity loss which are
effective in discriminating local feature embeddings from high-level feature
maps. We also analyzed the use of an adaptive part-weighting mechanism that
weighs the part-descriptors based on the mean activation of the part template-
attention weights. The qualitative and quantitative results show that the
dynamically initialed templates learns to localize human parts and provides
competitive results without using additional feature extractors or dataset
dependent part-initialization.

Our model can be further extended by utilizing the part-descriptors to compute
higher order topological structures for better representation or utilizing
backbones with stronger representation power. Further, implementation of a
distance metric that is able to utilize the adaptive weighting module may
improve occluded and partial Re-ID performance. The dynamic local feature
initialization can be generalized to many other vision tasks, such as object
detection during autonomous driving, by modeling and propagating important
local features at earlier layers of a backbone that leads to understand
challenging scenes better.

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