Learning to Decompose Visual Features with Latent Textual Prompts

• URL: http://arxiv.org/abs/2210.04287v1
• Date: Sun, 9 Oct 2022 15:40:13 GMT
• Title: Learning to Decompose Visual Features with Latent Textual Prompts
• Authors: Feng Wang, Manling Li, Xudong Lin, Hairong Lv, Alexander G. Schwing and Heng Ji
• Abstract summary: We propose Decomposed Feature Prompting (DeFo) to improve vision-language models. Our empirical study shows DeFo's significance in improving the vision-language models.
• Score: 140.2117637223449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based inference (the challenge for zero-shot protocol); or 2) breaking the well-established vision-language alignment (the challenge for linear probing). To address them, we propose Decomposed Feature Prompting (DeFo). DeFo leverages a flexible number of learnable embeddings as textual input while maintaining the vision-language dual-model architecture, which enables the model to learn decomposed visual features with the help of feature-level textual prompts. We further use an additional linear layer to perform classification, allowing a scalable size of language inputs. Our empirical study shows DeFo's significance in improving the vision-language models. For example, DeFo obtains 73.2% test accuracy on ImageNet with a ResNet-50 backbone without tuning any pretrained weights of both the vision and language encoder, outperforming zero-shot CLIP by a large margin of 15.0%, and outperforming state-of-the-art vision-language prompt tuning method by 7.6%.

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