Normalized Contrastive Learning for Text-Video Retrieval
Yookoon Park, Mahmoud Azab, Seungwhan Moon, Bo Xiong, Florian Metze, Gourab Kundu, Kirmani Ahmed
Abstract
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.- Anthology ID:
- 2022.emnlp-main.17
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 248–260
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.17
- DOI:
- Cite (ACL):
- Yookoon Park, Mahmoud Azab, Seungwhan Moon, Bo Xiong, Florian Metze, Gourab Kundu, and Kirmani Ahmed. 2022. Normalized Contrastive Learning for Text-Video Retrieval. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 248–260, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Normalized Contrastive Learning for Text-Video Retrieval (Park et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.emnlp-main.17.pdf