Colar: Effective and Efficient Online Action Detection by Consulting Exemplars

• URL: http://arxiv.org/abs/2203.01057v1
• Date: Wed, 2 Mar 2022 12:13:08 GMT
• Title: Colar: Effective and Efficient Online Action Detection by Consulting Exemplars
• Authors: Le Yang, Junwei Han, Dingwen Zhang
• Abstract summary: We develop an effective exemplar-consultation mechanism that first measures the similarity between a frame and exemplary frames, and then aggregates exemplary features based on the similarity weights. Due to the complementarity from the category-level modeling, our method employs a lightweight architecture but achieves new high performance on three benchmarks.
• Score: 102.28515426925621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online action detection has attracted increasing research interests in recent years. Current works model historical dependencies and anticipate future to perceive the action evolution within a video segment and improve the detection accuracy. However, the existing paradigm ignores category-level modeling and does not pay sufficient attention to efficiency. Considering a category, its representative frames exhibit various characteristics. Thus, the category-level modeling can provide complementary guidance to the temporal dependencies modeling. In this paper, we develop an effective exemplar-consultation mechanism that first measures the similarity between a frame and exemplary frames, and then aggregates exemplary features based on the similarity weights. This is also an efficient mechanism as both similarity measurement and feature aggregation require limited computations. Based on the exemplar-consultation mechanism, the long-term dependencies can be captured by regarding historical frames as exemplars, and the category-level modeling can be achieved by regarding representative frames from a category as exemplars. Due to the complementarity from the category-level modeling, our method employs a lightweight architecture but achieves new high performance on three benchmarks. In addition, using a spatio-temporal network to tackle video frames, our method spends 9.8 seconds to dispose of a one-minute video and achieves comparable performance.

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