Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization

• URL: http://arxiv.org/abs/2108.02183v1
• Date: Wed, 4 Aug 2021 17:16:18 GMT
• Title: Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
• Authors: Rui Qian, Yuxi Li, Huabin Liu, John See, Shuangrui Ding, Xian Liu, Dian Li, Weiyao Lin
• Abstract summary: This paper proposes a multi-level feature optimization framework to improve the generalization and temporal modeling ability of learned video representations. Experiments demonstrate that multi-level feature optimization with the graph constraint and temporal modeling can greatly improve the representation ability in video understanding.
• Score: 30.670109727802494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics and neglected lower-level representations and their temporal relationship which are crucial for general video understanding. To address these challenges, this paper proposes a multi-level feature optimization framework to improve the generalization and temporal modeling ability of learned video representations. Concretely, high-level features obtained from naive and prototypical contrastive learning are utilized to build distribution graphs, guiding the process of low-level and mid-level feature learning. We also devise a simple temporal modeling module from multi-level features to enhance motion pattern learning. Experiments demonstrate that multi-level feature optimization with the graph constraint and temporal modeling can greatly improve the representation ability in video understanding.

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