Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics

• URL: http://arxiv.org/abs/2208.00475v1
• Date: Sun, 31 Jul 2022 17:36:09 GMT
• Title: Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics
• Authors: Xiaoyuan Guo, Jiali Duan, C.-C. Jay Kuo, Judy Wawira Gichoya and Imon Banerjee
• Abstract summary: We 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. Experiments validate the effectiveness of our approach across common vision-language benchmarks.
• Score: 29.393661499333284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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