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In the field of cross-modal retrieval, single encoder models tend to performbetter than dual encoder models, but they suffer from high latency and lowthroughput. In this paper, we present a dual encoder model called BagFormerthat utilizes a cross modal interaction mechanism to improve recall performancewithout sacrificing latency and throughput. BagFormer achieves this through theuse of bag-wise interactions, which allow for the transformation of text to amore appropriate granularity and the incorporation of entity knowledge into themodel. Our experiments demonstrate that BagFormer is able to achieve resultscomparable to state-of-the-art single encoder models in cross-modal retrievaltasks, while also offering efficient training and inference with 20.72 timeslower latency and 25.74 times higher throughput.

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