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Motion Sensitive Contrastive Learning for Self-supervised Video Representation. (arXiv:2208.06105v1 [cs.CV])
Aug. 15, 2022, 1:11 a.m. | Jingcheng Ni, Nan Zhou, Jie Qin, Qian Wu, Junqi Liu, Boxun Li, Di Huang
cs.CV updates on arXiv.org arxiv.org
Contrastive learning has shown great potential in video representation
learning. However, existing approaches fail to sufficiently exploit short-term
motion dynamics, which are crucial to various down-stream video understanding
tasks. In this paper, we propose Motion Sensitive Contrastive Learning (MSCL)
that injects the motion information captured by optical flows into RGB frames
to strengthen feature learning. To achieve this, in addition to clip-level
global contrastive learning, we develop Local Motion Contrastive Learning
(LMCL) with frame-level contrastive objectives across the two modalities. …
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