Vision Learners Meet Web Image-Text Pairs

• URL: http://arxiv.org/abs/2301.07088v3
• Date: Mon, 5 Aug 2024 15:38:05 GMT
• Title: Vision Learners Meet Web Image-Text Pairs
• Authors: Bingchen Zhao, Quan Cui, Hao Wu, Osamu Yoshie, Cheng Yang, Oisin Mac Aodha,
• Abstract summary: In this work, we consider self-supervised pre-training on noisy web sourced image-text paired data. We compare a range of methods, including single-modal ones that use masked training objectives and multi-modal ones that use image-text constrastive training. We present a new visual representation pre-training method, MUlti-modal Generator(MUG), that learns from scalable web sourced image-text data.
• Score: 32.36188289972377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many self-supervised learning methods are pre-trained on the well-curated ImageNet-1K dataset. In this work, given the excellent scalability of web data, we consider self-supervised pre-training on noisy web sourced image-text paired data. First, we conduct a benchmark study of representative self-supervised pre-training methods on large-scale web data in a like-for-like setting. We compare a range of methods, including single-modal ones that use masked training objectives and multi-modal ones that use image-text constrastive training. We observe that existing multi-modal methods do not outperform their single-modal counterparts on vision transfer learning tasks. We derive an information-theoretical view to explain these benchmark results, which provides insight into how to design a novel vision learner. Inspired by this insight, we present a new visual representation pre-training method, MUlti-modal Generator~(MUG), that learns from scalable web sourced image-text data. MUG achieves state-of-the-art transfer performance on a variety of tasks and demonstrates promising scaling properties. Pre-trained models and code will be made public upon acceptance.

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