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arXiv · 2212.14322

BagFormer: Better Cross-Modal Retrieval via bag-wise interaction

Haowen Hou·Xiaopeng Yan·Yigeng Zhang·Fengzong Lian·Zhanhui Kang
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Abstract

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.

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Haowen Hou, Xiaopeng Yan, Yigeng Zhang, Fengzong Lian, Zhanhui Kang. 2022-12-29. BagFormer: Better Cross-Modal Retrieval via bag-wise interaction. https://arxiv.org/abs/2212.14322

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