Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness. In this work, however, we reveal that cross-modal contrastive learning suffers from incorrect normalization of the sum retrieval probabilities of each text or video instance. Specifically, we show that many test instances are either over- or under-represented during retrieval, significantly hurting the retrieval performance. To address this problem, we propose Normalized Contrastive Learning (NCL) which utilizes the Sinkhorn-Knopp algorithm to compute the instance-wise biases that properly normalize the sum retrieval probabilities of each instance so that every text and video instance is fairly represented during cross-modal retrieval. Empirical study shows that NCL brings consistent and significant gains in text-video retrieval on different model architectures, with new state-of-the-art multimodal retrieval metrics on the ActivityNet, MSVD, and MSR-VTT datasets without any architecture engineering.
@article{arxiv.2212.11790,
title = {Normalized Contrastive Learning for Text-Video Retrieval},
author = {Yookoon Park and Mahmoud Azab and Bo Xiong and Seungwhan Moon and Florian Metze and Gourab Kundu and Kirmani Ahmed},
journal= {arXiv preprint arXiv:2212.11790},
year = {2022}
}