Few-Shot Action Recognition with Compromised Metric via Optimal Transport

• URL: http://arxiv.org/abs/2104.03737v1
• Date: Thu, 8 Apr 2021 12:42:05 GMT
• Title: Few-Shot Action Recognition with Compromised Metric via Optimal Transport
• Authors: Su Lu, Han-Jia Ye, De-Chuan Zhan
• Abstract summary: Few-shot action recognition is still not mature despite the wide research of few-shot image classification. One main obstacle to applying these algorithms in action recognition is the complex structure of videos. We propose Compromised Metric via Optimal Transport (CMOT) to combine the advantages of these two solutions.
• Score: 31.834843714684343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although vital to computer vision systems, few-shot action recognition is still not mature despite the wide research of few-shot image classification. Popular few-shot learning algorithms extract a transferable embedding from seen classes and reuse it on unseen classes by constructing a metric-based classifier. One main obstacle to applying these algorithms in action recognition is the complex structure of videos. Some existing solutions sample frames from a video and aggregate their embeddings to form a video-level representation, neglecting important temporal relations. Others perform an explicit sequence matching between two videos and define their distance as matching cost, imposing too strong restrictions on sequence ordering. In this paper, we propose Compromised Metric via Optimal Transport (CMOT) to combine the advantages of these two solutions. CMOT simultaneously considers semantic and temporal information in videos under Optimal Transport framework, and is discriminative for both content-sensitive and ordering-sensitive tasks. In detail, given two videos, we sample segments from them and cast the calculation of their distance as an optimal transport problem between two segment sequences. To preserve the inherent temporal ordering information, we additionally amend the ground cost matrix by penalizing it with the positional distance between a pair of segments. Empirical results on benchmark datasets demonstrate the superiority of CMOT.

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