Robust Cross-Modal Representation Learning with Progressive Self-Distillation

TL;DR

This paper introduces a novel cross-modal contrastive learning framework with progressive self-distillation that enhances robustness and efficiency in learning from noisy web-harvested image-caption data, outperforming CLIP across multiple benchmarks.

Contribution

The proposed method employs progressive self-distillation with soft alignments to improve robustness and data efficiency in vision-language models from noisy datasets.

Findings

Outperforms CLIP on zero-shot classification, transfer, and retrieval tasks.

Offers improved robustness to natural distribution shifts.

Scales effectively with larger training datasets.

Abstract

The learning objective of vision-language approach of CLIP does not effectively account for the noisy many-to-many correspondences found in web-harvested image captioning datasets, which contributes to its compute and data inefficiency. To address this challenge, we introduce a novel training framework based on cross-modal contrastive learning that uses progressive self-distillation and soft image-text alignments to more efficiently learn robust representations from noisy data. Our model distills its own knowledge to dynamically generate soft-alignment targets for a subset of images and captions in every minibatch, which are then used to update its parameters. Extensive evaluation across 14 benchmark datasets shows that our method consistently outperforms its CLIP counterpart in multiple settings, including: (a) zero-shot classification, (b) linear probe transfer, and (c) image-text retrieval, without incurring added computational cost. Analysis using an ImageNet-based robustness test-bed reveals that our method offers better effective robustness to natural distribution shifts compared to both ImageNet-trained models and CLIP itself. Lastly, pretraining with datasets spanning two orders of magnitude in size shows that our improvements over CLIP tend to scale with number of training examples.