Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos

Autor:	Fan, David, Yang, Deyu, Li, Xinyu, Bhat, Vimal, MV, Rohith
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Contrastive learning has recently narrowed the gap between self-supervised and supervised methods in image and video domain. State-of-the-art video contrastive learning methods such as CVRL and $\rho$-MoCo spatiotemporally augment two clips from the same video as positives. By only sampling positive clips locally from a single video, these methods neglect other semantically related videos that can also be useful. To address this limitation, we leverage nearest-neighbor videos from the global space as additional positive pairs, thus improving positive key diversity and introducing a more relaxed notion of similarity that extends beyond video and even class boundaries. Our method, Inter-Intra Video Contrastive Learning (IIVCL), improves performance on a range of video tasks. Comment: Accepted to the ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.07317 Zobrazit plný text záznamu View this record from Arxiv