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Learning Customized Visual Models with Retrieval-Augmented Knowledge
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2026-02-20 08:16:24
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Learning Customized Visual Models with Retrieval-Augmented Knowledge R EACT: Learning Customized Visual Models with Retrieval-Augmented Knowledge Haotian Liu , Kilho Son , Jianwei Yang , Ce Liu , Jianfeng Gao , Yong Jae Lee * , Chunyuan Li * ▶ University of Wisconsin-Madison; ▶ Microsoft    * Equal Advising arXiv Code Introducing a customization stage to the lifecycle of foundation models! R EACT customizes foundation models to downstream tasks without the need of any labeled data. Abstract Image-text contrastive learning models such as CLIP and OpenCLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weights. Alternatively, we propose R EACT , RE trieval- A ugmented C us T omization...
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