Suit the Remedy to the Retriever: Interpretable Query Optimization with Retriever Preference Alignment for Vision-Language Retrieval
GuangHao Meng, Jinpeng Wang, Jieming Zhu, Letian Zhang, Yong Jiang, Dan Zhao, Qing Li
Abstract
Vision-language retrieval (VLR), which uses text or image queries to retrieve corresponding cross-modal content, plays a crucial role in multimedia and computer vision tasks. However, challenging concepts in queries often confuse retrievers, limiting their ability to align concepts with visual content. Existing query optimization methods neglect retrievers’ preferences (i.e., text descriptions that better match their corresponding visual content), resulting in unadapted to the retriever and leading to suboptimal performance. To address this, we propose the Retriever-Adaptive Query Optimization (RAQO), an interpretable framework that rewrites queries based on retriever-specific preferences. Specifically, we first leverages multimodal large language Models (MLLMs) and retrieval
BibTeX
@inproceedings{aaai2026_suittheremedytot,
title = {Suit the Remedy to the Retriever: Interpretable Query Optimization with Retriever Preference Alignment for Vision-Language Retrieval},
author = {GuangHao Meng and Jinpeng Wang and Jieming Zhu and Letian Zhang and Yong Jiang and Dan Zhao and Qing Li},
booktitle = {AAAI 2026},
year = {2026}
}