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A Circular Window-based Cascade Transformer for Online Action Detection

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Shuqiang Cao, Weixin Luo, Bairui Wang, Wei Zhang, Lin Ma

Computer Vision and Pattern Recognition · Submitted 30 Aug 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our proposed method achieves the state-of-the-art performances on three challenging datasets THUMOS'14, TVSeries, and HDD.

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arXiv ID2208.14209 (version 1)
Pith IDN2T36DCK, Pith's permanent record of this paper and its review
CitationBairui Wang, Lin Ma, Shuqiang Cao, Weixin Luo, Wei Zhang. "A Circular Window-based Cascade Transformer for Online Action Detection." arXiv:2208.14209 (2022). https://pith.science/paper/N2T36DCK
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,652 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.17 · human participants 0.03 · controlled comparison 0.15 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.65 · mentions code or data availability 0.38.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Online action detection aims at the accurate action prediction of the current frame based on long historical observations. Meanwhile, it demands real-time inference on online streaming videos. In this paper, we advocate a novel and efficient principle for online action detection. It merely updates the latest and oldest historical representations in one window but reuses the intermediate ones, which have been already computed. Based on this principle, we introduce a window-based cascade Transformer with a circular historical queue, where it conducts multi-stage attentions and cascade refinement on each window. We also explore the association between online action detection and its counterpart offline action segmentation as an auxiliary task. We find that such an extra supervision helps discriminative history clustering and acts as feature augmentation for better training the classifier and cascade refinement. Our proposed method achieves the state-of-the-art performances on three challenging datasets THUMOS'14, TVSeries, and HDD. Codes will be available after acceptance.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Online action detection aims at the accurate action prediction of the current frame based on long historical observations. background 1.00 0.05 0.02 asserted 1.00
s1 Meanwhile, it demands real-time inference on online streaming videos. background 1.00 0.06 0.02 asserted 1.00
s2 In this paper, we advocate a novel and efficient principle for online action detection. contribution 0.99 0.57 0.06 asserted 0.88
s3 It merely updates the latest and oldest historical representations in one window but reuses the intermediate ones, which have been already computed. method 0.84 0.61 0.08 asserted 1.00
s4 Based on this principle, we introduce a window-based cascade Transformer with a circular historical queue, where it conducts multi-stage attentions and cascade refinement on each window. method 0.84 0.90 0.07 asserted 1.00
s5 We also explore the association between online action detection and its counterpart offline action segmentation as an auxiliary task. contribution 0.69 0.65 0.03 asserted 0.58
s6 We find that such an extra supervision helps discriminative history clustering and acts as feature augmentation for better training the classifier and cascade refinement. result 0.52 0.96 0.10 asserted 1.00
s7 Our proposed method achieves the state-of-the-art performances on three challenging datasets THUMOS'14, TVSeries, and HDD. result 1.00 0.96 0.73 asserted 1.00
s8 Codes will be available after acceptance. future work 0.85 0.19 0.02 asserted 0.88

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

What was examined, and what was not

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Online action detection aims at the accurate action prediction of the current frame based on long historical observations. Meanwhile, it demands real-time inference on online streaming videos. In this paper, we advocate a novel and efficient principle for online action detection. It merely updates the latest and oldest historical representations in one window but reuses the intermediate ones, which have been already computed. Based on this principle, we introduce a window-based cascade Transformer with a circular historical queue, where it conducts multi-stage attentions and cascade refinement on each window. We also explore the association between online action detection and its counterpart offline action segmentation as an auxiliary task. We find that such an extra supervision helps discriminative history clustering and acts as feature augmentation for better training the classifier and cascade refinement. Our proposed method achieves the state-of-the-art performances on three challenging datasets THUMOS'14, TVSeries, and HDD. Codes will be available after acceptance.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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  • With the advent of Transformer models demonstrating powerful modeling capabilities in computer vision, researchers have designed various Transformer architectures for end-to-end online action detection [ref] .

    Driving behavior recognition via self-discovery learning · Driving Behavior Recognition · 0.86

What it cites · 11 sentences without a confident label

Uses the method or tool of 2 papers
Compares with 3 papers
Cites as background 12 papers

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