TriDet: Temporal Action Detection with Relative Boundary Modeling
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Abstract (arXiv)
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.
Key facts. Cited by 141 papers in the map (data through 7 Oct 2026). Builds on 48 of its 56 references that are in the map. First posted on arXiv 13 Mar 2023.
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48 of 56 references.
References · 48
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- 2018Diagnosing Error in Temporal Action Detectorscited by 71
- 2021Enriching Local and Global Contexts for Temporal Action Localizationcited by 64
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- 2022ReAct: Temporal Action Detection with Relational Queriescited by 47
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- 2022An Efficient Spatio-Temporal Pyramid Transformer for Action Detectioncited by 16
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Citing papers per year: 2023: 20; 2024: 47; 2025: 45; 2026: 29.
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