In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput. In this paper, we present a dual encoder model called BagFormer that utilizes a cross modal interaction mechanism to improve recall performance without sacrificing latency and throughput. BagFormer achieves this through the use of bag-wise interactions, which allow for the transformation of text to a more appropriate granularity and the incorporation of entity knowledge into the model. Our experiments demonstrate that BagFormer is able to achieve results comparable to state-of-the-art single encoder models in cross-modal retrieval tasks, while also offering efficient training and inference with 20.72 times lower latency and 25.74 times higher throughput.
@article{arxiv.2212.14322,
title = {BagFormer: Better Cross-Modal Retrieval via bag-wise interaction},
author = {Haowen Hou and Xiaopeng Yan and Yigeng Zhang and Fengzong Lian and Zhanhui Kang},
journal= {arXiv preprint arXiv:2212.14322},
year = {2023}
}