SimVLM
Simple Visual Language Model
SimVLM is a minimalist pretraining framework to reduce training complexity by exploiting large-scale weak supervision. It is trained end-to-end with a single prefix language modeling (PrefixLM) objective. PrefixLM enables bidirectional attention within the prefix sequence, and thus it is applicable for both decoder-only
and encoder-decoder sequence-to-sequence language models.
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://paperswithcode.com/paper/simvlm-simple-visual-language-model
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision