@inproceedings{zhang2024irgen,
author = {Zhang, Yidan and Zhang, Ting and Chen, Dong and Wang, Yujing and Chen, Qi and Xie, Xing and Sun, Hao and Deng, Weiwei and Zhang, Qi and Yang, Fan and Yang, Mao and Liao, Qingmin and Wang, Jingdong and Guo, Baining},
title = {IRGen: Generative Modeling for Image Retrieval},
booktitle = {ECCV'24},
year = {2024},
month = {October},
abstract = {While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified. In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling and employing a sequence-to-sequence model. This approach is harmoniously aligned with the current trend towards unification in research, presenting a cohesive framework that allows for end-to-end differentiable searching. This, in turn, facilitates superior performance via direct optimization techniques. The development of our model, dubbed IRGen, addresses the critical technical challenge of converting an image into a concise sequence of semantic units, which is pivotal for enabling efficient and effective search. Extensive experiments demonstrate that our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks as well as two million-scale datasets, yielding significant improvement compared to prior competitive retrieval methods. In addition, the notable surge in precision scores facilitated by generative modeling presents the potential to bypass the reranking phase, which is traditionally indispensable in practical retrieval workflows. The code is publicly available at https://github.com/yakt00/IRGen.},
url = {http://approjects.co.za/?big=en-us/research/publication/irgen-generative-modeling-for-image-retrieval/},
}