ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval

• URL: http://arxiv.org/abs/2502.15682v2
• Date: Thu, 27 Mar 2025 17:57:43 GMT
• Title: ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval
• Authors: Guanqi Zhan, Yuanpei Liu, Kai Han, Weidi Xie, Andrew Zisserman,
• Abstract summary: We introduce a new framework that can boost the performance of large-scale pre-trained vision- curation models.<n>The approach, Enhanced Language-Image Pre-training (ELIP), uses the text query, via a simple mapping network, to predict a set of visual prompts.<n>ELIP can easily be applied to the commonly used CLIP, SigLIP and BLIP-2 networks.
• Score: 83.01358520910533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective in this paper is to improve the performance of text-to-image retrieval. To this end, we introduce a new framework that can boost the performance of large-scale pre-trained vision-language models, so that they can be used for text-to-image re-ranking. The approach, Enhanced Language-Image Pre-training (ELIP), uses the text query, via a simple MLP mapping network, to predict a set of visual prompts to condition the ViT image encoding. ELIP can easily be applied to the commonly used CLIP, SigLIP and BLIP-2 networks. To train the architecture with limited computing resources, we develop a 'student friendly' best practice, involving global hard sample mining, and curation of a large-scale dataset. On the evaluation side, we set up two new out-of-distribution (OOD) benchmarks, Occluded COCO and ImageNet-R, to assess the zero-shot generalisation of the models to different domains. The results demonstrate that ELIP significantly boosts CLIP/SigLIP/SigLIP-2 text-to-image retrieval performance and outperforms BLIP-2 on several benchmarks, as well as providing an easy means to adapt to OOD datasets.

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