Multi-modal Alignment using Representation Codebook

TL;DR

This paper introduces a novel multi-modal alignment method using a shared representation codebook and contrastive learning, achieving state-of-the-art results in zero-shot cross-modal retrieval and competitive performance on transfer tasks.

Contribution

It proposes a cluster-based alignment approach with a teacher-student distillation paradigm for more stable and effective vision-language representation learning.

Findings

Achieves new state-of-the-art in zero-shot cross-modal retrieval.

Performs competitively on various transfer tasks.

Demonstrates improved stability in multi-modal alignment.

Abstract

Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion. Since image and text typically reside in different regions of the feature space, directly aligning them at instance level is challenging especially when features are still evolving during training. In this paper, we propose to align at a higher and more stable level using cluster representation. Specifically, we treat image and text as two "views" of the same entity, and encode them into a joint vision-language coding space spanned by a dictionary of cluster centers (codebook). We contrast positive and negative samples via their cluster assignments while simultaneously optimizing the cluster centers. To further smooth out the learning process, we adopt a teacher-student distillation paradigm, where the momentum teacher of one view guides the student learning of the other. We evaluated our approach on common vision language benchmarks and obtain new SoTA on zero-shot cross modality retrieval while being competitive on various other transfer tasks.