Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics
This work addresses a fundamental bottleneck in multimodal AI by discretizing visual representations to better integrate with language, though it builds incrementally on existing methods like VQ-VAE.
The paper tackles the challenge of aligning continuous visual data with discrete language in vision-language pretraining by learning a semantic codebook for visual tokens, achieving improved performance on standard benchmarks.
Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning. In contrast, visual modality is inherently continuous and high-dimensional, which potentially prohibits the alignment as well as fusion between vision and language modalities. We therefore propose to "discretize" the visual representation by joint learning a codebook that imbues each visual token a semantic. We then utilize these discretized visual semantics as self-supervised ground-truths for building our Masked Image Modeling objective, a counterpart of Masked Language Modeling which proves successful for language models. To optimize the codebook, we extend the formulation of VQ-VAE which gives a theoretic guarantee. Experiments validate the effectiveness of our approach across common vision-language benchmarks.