Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

• URL: http://arxiv.org/abs/2301.02903v1
• Date: Sat, 7 Jan 2023 17:24:11 GMT
• Title: Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
• Authors: Byoungjip Kim, Sungik Choi, Dasol Hwang, Moontae Lee, Honglak Lee
• Abstract summary: In this paper, we propose a method that can effectively transfer the representations of a large pre-trained multimodal model into a small target model. For unsupervised transfer, we introduce cross-modal similarity matching (CSM) that enables a student model to learn the representations of a teacher model. To better encode the text prompts, we design context-based prompt augmentation (CPA) that can alleviate the lexical ambiguity of input text prompts.
• Score: 49.730741713652435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources. In this paper, we propose a method (BeamCLIP) that can effectively transfer the representations of a large pre-trained multimodal model (CLIP-ViT) into a small target model (e.g., ResNet-18). For unsupervised transfer, we introduce cross-modal similarity matching (CSM) that enables a student model to learn the representations of a teacher model by matching the relative similarity distribution across text prompt embeddings. To better encode the text prompts, we design context-based prompt augmentation (CPA) that can alleviate the lexical ambiguity of input text prompts. Our experiments show that unsupervised representation transfer of a pre-trained vision-language model enables a small ResNet-18 to achieve a better ImageNet-1K top-1 linear probe accuracy (66.2%) than vision-only self-supervised learning (SSL) methods (e.g., SimCLR: 51.8%, SwAV: 63.7%), while closing the gap with supervised learning (69.8%).

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