Long Short-Term Transformer for Online Action Detection
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Abstract (arXiv)
We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data. It consists of an LSTR encoder that dynamically leverages coarse-scale historical information from an extended temporal window (e.g., 2048 frames spanning of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 frames spanning 8 seconds) to model the fine-scale characteristics of the data. Compared to prior work, LSTR provides an effective and efficient method to model long videos with fewer heuristics, which is validated by extensive empirical analysis. LSTR achieves state-of-the-art performance on three standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment. Code has been made available at: https://xumingze0308.github.io/projects/lstr
Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). Builds on 55 of its 75 references that are in the map. First posted on arXiv 7 Jul 2021.
Builds on
55 of 75 references.
References · 55
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- 2017Temporal Action Detection with Structured Segment Networkscited by 265
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- 2016The THUMOS Challenge on Action Recognition for Videos "in the Wild"cited by 213
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- 2018Temporal Recurrent Networks for Online Action Detectioncited by 79
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- 2016Online Action Detectioncited by 68
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- 2021Activity Graph Transformer for Temporal Action Localizationcited by 48
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50 most cited of 55 All 55 (JSON)
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Citing papers per year: 2021: 3; 2022: 18; 2023: 16; 2024: 17; 2025: 27; 2026: 6.
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