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TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
Open accessAbstract
Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e.g. human actions) segments from untrimmed videos is an important step for large-scale video analysis. We propose a novel Temporal Unit Regression Network (TURN) model. There are two salient aspects of TURN: (1) TURN jointly predicts action proposals and refines the temporal boundaries by temporal coordinate regression: (2) Fast computation is enabled by unit feature reuse: a long untrimmed video is decomposed into video units, which are reused as basic building blocks of temporal proposals. TURN outperforms the previous state-of-the-art methods under average recall (AR) by a large margin on THUMOS-14 and ActivityNet datasets, and runs at over 880 frames per second (FPS) on a TITAN X GPU. We further apply TURN as a proposal generation stage for existing temporal action localization pipelines, it outperforms state-of-the-art performance on THUMOS-14 and ActivityNet.
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“Deepfake localization approaches are either anchor-based [21, 22] that utilize a sliding window approach to detect deepfake segments or boundary prediction based [6, 10, 9, 64] that focuses on predicting the boundary of fake segments in a video using boundary matching loss.”
From Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization (2024) · cites this paper as [21]
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Foo, L. G., Li, T., Rahmani, H., & Liu, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01737 · Cited by 18
SRF-Net: Selective Receptive Field Network for Anchor-Free Temporal Action Detection
Ning, R., Zhang, C., & Zou, Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414253 · Cited by 17
Zhu, Z., Wang, L., Tang, W., Liu, Z., Zheng, N., & Hua, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20277 · Cited by 17
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation
Vo-Ho, V.-K., Joo, H., Yamazaki, K., Truong, S. Q., Kitani, K., Tran, M., & Le, N. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.306 · Cited by 17
Ju, C., Zhao, P., Zhang, Y., Wang, Y., & Tian, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.08236 · Cited by 17
Capsule Boundary Network With 3D Convolutional Dynamic Routing for Temporal Action Detection
Chen, Y., Guo, B., Shen, Y., Wang, W., Lu, W., & Suo, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3104226 · Cited by 16
Beyond the Premier: Assessing Action Spotting Transfer Capability Across Diverse Domains
Cabado, B., Cioppa, A., Giancola, S., Villa, A., Guijarro‐Berdiñas, B., Padrón, E. J., Ghanem, B., & Droogenbroeck, M. V. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00343 · Cited by 17
SLTFNet: A spatial and language-temporal tensor fusion network for video moment retrieval
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Play and rewind: Context-aware video temporal action proposals
Gao, L., Li, T., Song, J., Zhao, Z., & Shen, H. T. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107477 · Cited by 16
Enhanced Attention Tracking With Multi-Branch Network for Egocentric Activity Recognition
Liu, T., Lam, K., Zhao, R., & Kong, J. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3104651 · Cited by 16
From Coarse to Fine: Hierarchical Structure-aware Video Summarization
Li, W., Pan, G., Wang, C., Xing, Z., & Han, Z. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3485472 · Cited by 16
Metadata
- Authors
- Gao, J., Yang, Z., Sun, C., Chen, K., & Nevatia, R.
- Affiliations
- University of Southern California (US)
- Google (United States) (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3648-3656
- OpenAlex
- W2597958930
- Cited by
- 498
- References
- 40
- Citations by year
- 2026: 8 · 2025: 26 · 2024: 36 · 2023: 46 · 2022: 78 · 2021: 107 · 2020: 75 · 2019: 83 · 2018: 37 · 2017: 2
- FWCI
- 15.75
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- feature reuse, temporal action localization, real-time video processing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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