TriDet: Temporal Action Detection with Relative Boundary Modeling

• URL: http://arxiv.org/abs/2303.07347v2
• Date: Thu, 16 Mar 2023 11:26:39 GMT
• Title: TriDet: Temporal Action Detection with Relative Boundary Modeling
• Authors: Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, Dacheng Tao
• Abstract summary: Existing methods often suffer from imprecise boundary predictions due to ambiguous action boundaries in videos. We propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. TriDet achieves state-of-the-art performance on three challenging benchmarks.
• Score: 85.49834276225484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action boundaries in videos. To alleviate this problem, we propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. In the feature pyramid of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer to mitigate the rank loss problem of self-attention that takes place in the video features and aggregate information across different temporal granularities. Benefiting from the Trident-head and the SGP-based feature pyramid, TriDet achieves state-of-the-art performance on three challenging benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational costs, compared to previous methods. For example, TriDet hits an average mAP of $69.3\%$ on THUMOS14, outperforming the previous best by $2.5\%$, but with only $74.6\%$ of its latency. The code is released to https://github.com/sssste/TriDet.

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