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 or boundary-prediction based.
“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]
Section 3.2, “Clip Pyramid Modeling” (page unavailable), describes scanning videos with temporal windows at each anchor unit, supporting the sliding-window aspect of the citation. But the paper applies this method to temporal action proposals, not deepfake segments; it does not support the claim about deepfake localization approaches. (LLM, conf=0.87)
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Self-citationSu, H., Gan, W., Wu, W., Qiao, Y., & Yan, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16363 · Cited by 127 · see citing works
Weakly Supervised Temporal Action Localization Through Contrast Based Evaluation Networks
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TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection
Zhang, L., Chang, X., Liu, J., Luo, M., Li, Z., Yao, L., & Hauptmann, A. G. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183586 · Cited by 106 · see citing works
A Context-Aware Loss Function for Action Spotting in Soccer Videos
Cioppa, A., Deliège, A., Giancola, S., Ghanem, B., Droogenbroeck, M. V., Gade, R., & Moeslund, T. B. (2020) · 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/cvpr42600.2020.01314 · Cited by 104 · see citing works
Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences
Zhang, Z., Zhao, Z., Zhao, Y., Wang, Q., Liu, H., & Gao, L. (2020) · 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/cvpr42600.2020.01068 · Cited by 104 · see citing works
Step-by-step Erasion, One-by-one Collection
Zhong, J.-X., Li, N., Kong, W., Zhang, T., Li, T. H., & Li, G. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240511 · Cited by 103 · see citing works
Video Moment Localization via Deep Cross-Modal Hashing
Hu, Y., Liu, M., Su, X., Gao, Z., & Nie, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3073867 · Cited by 100 · see citing works
Cai, Z., Stefanov, K., Dhall, A., & Hayat, M. (2022) · 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034605 · Cited by 99 · see citing works
Diagnosing Error in Temporal Action Detectors
Alwassel, H., Heilbron, F. C., Escorcia, V. A., & Ghanem, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_16 · Cited by 98 · see citing works
Long Short-Term Transformer for Online Action DetectionPreprint
Xu, M., Xiong, Y., Chen, H., Li, X., Xia, W., Tu, Z., & Soatto, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03377 · Cited by 98 · see citing works
A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation
Amin, F. M., Rezayati, M., van de Venn, H. W., & Karimpour, H. (2020) · Sensors · doi:10.3390/s20216347 · Cited by 96 · see citing works
ReAct: Temporal Action Detection with Relational Queries
Shi, D., Zhong, Y., Cao, Q., Zhang, J., Ma, L., Li, J., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_7 · Cited by 94 · see citing works
Deep Learning-Based Action Detection in Untrimmed Videos: A Survey
Vahdani, E., & Tian, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3193611 · Cited by 93 · see citing works
Multi-shot Temporal Event Localization: a Benchmark
Liu, X., Hu, Y. G., Bai, S., Ding, F., Bai, X., & Torr, P. H. S. (2021) · 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/cvpr46437.2021.01241 · Cited by 92 · see citing works
Cross-Modal Video Moment Retrieval with Spatial and Language-Temporal Attention
Jiang, B., Huang, X., Yang, C., & Yuan, J. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3325019 · Cited by 89 · see citing works
Graph Convolutional Module for Temporal Action Localization in Videos
Zeng, R., Huang, W., Tan, M., Rong, Y., Zhao, P., Huang, J., & Gan, C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3090167 · Cited by 89 · see citing works
Weakly Supervised Temporal Adjacent Network for Language Grounding
Wang, Y., Deng, J., Zhou, W., & Li, H. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3096087 · Cited by 88 · see citing works
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
Liu, Z., Wang, L., Zhang, Q., Tang, W., Yuan, J., Zheng, N., & Hua, G. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16322 · Cited by 84 · see citing works
FineAction: A Fine-Grained Video Dataset for Temporal Action Localization
Liu, Y., Wang, L., Wang, Y., Ma, X., & Qiao, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3217368 · Cited by 84 · see citing works
WSLLN:Weakly Supervised Natural Language Localization Networks
Gao, M., Davis, L. S., Socher, R., & Xiong, C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1157 · Cited by 81 · see citing works
Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization
Alwassel, H., Heilbron, F. C., & Ghanem, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_16 · Cited by 80 · see citing works
An Empirical Study of End-to-End Temporal Action Detection
Liu, X., Bai, S., & Bai, X. (2022) · 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/cvpr52688.2022.01938 · Cited by 79 · see citing works
Object-Region Video Transformers
Herzig, R., Avraham, E. B., Mangalam, K., Bar, A., Chechik, G., Rohrbach, A., Darrell, T., & Globerson, A. (2022) · 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/cvpr52688.2022.00315 · Cited by 77 · see citing works
Background-Click Supervision for Temporal Action Localization
Yang, L., Han, J., Zhao, T., Lin, T., Zhang, D., & Chen, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3132058 · Cited by 77 · see citing works
Online Detection of Action Start in Untrimmed, Streaming Videos
Shou, Z., Pan, J., Chan, J., Miyazawa, K., Mansour, H., Vetro, A., Giró-i-Nieto, X., & Chang, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_33 · Cited by 76 · see citing works
Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification
Liu, W., Zhang, C., Ma, H., & Li, S. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9362-4 · Cited by 74 · see citing works
Relation Attention for Temporal Action Localization
Chen, P., Gan, C., Shen, G., Huang, W., Zeng, R., & Tan, M. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2959977 · Cited by 74 · see citing works
Frame-Wise Cross-Modal Matching for Video Moment Retrieval
Tang, H., Zhu, J., Liu, M., Gao, Z., & Cheng, Z. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3063631 · Cited by 74 · see citing works
Coarse-to-Fine Semantic Alignment for Cross-Modal Moment Localization
Hu, Y., Nie, L., Liu, M., Wang, K., Wang, Y., & Hua, X. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3090521 · Cited by 74 · see citing works
DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Chen, G., Zheng, Y.-D., Wang, L., & Lü, T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19900 · Cited by 73 · see citing works
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
Wang, X., Zhang, S., Qing, Z., Shao, Y., Gao, C., & Sang, N. (2021) · 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/cvpr46437.2021.00194 · Cited by 72 · see citing works
Temporal Action Localization in the Deep Learning Era: A Survey
Wang, B., Zhao, Y., Yang, L., Long, T., & Li, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3330794 · Cited by 72 · see citing works
Learning Temporal Co-Attention Models for Unsupervised Video Action Localization
Gong, G., Wang, X., Mu, Y., & Tian, Q. C. (2020) · 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/cvpr42600.2020.00984 · Cited by 71 · see citing works
A Survey on Temporal Action Localization
Xia, H., & Zhan, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2986861 · Cited by 68 · see citing works
VLG-Net: Video-Language Graph Matching Network for Video Grounding
Soldan, M., Xu, M., Qu, S., Tegner, J. N., & Ghanem, B. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00361 · Cited by 67 · see citing works
Activity Graph Transformer for Temporal Action LocalizationPreprint
Nawhal, M., & Mori, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.08540 · Cited by 67 · see citing works
GateHUB: Gated History Unit with Background Suppression for Online Action Detection
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Modeling Multi-Label Action Dependencies for Temporal Action Localization
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Zero-Shot Temporal Action Detection via Vision-Language Prompting
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SSNet: Scale Selection Network for Online 3D Action Prediction
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Long-Term Video Question Answering via Multimodal Hierarchical Memory Attentive Networks
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StartNet: Online Detection of Action Start in Untrimmed Videos
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ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action LocalizationPreprint
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WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
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Graph Attention Based Proposal 3D ConvNets for Action Detection
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Video Summarization Via Actionness Ranking
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Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning
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Decoupling Localization and Classification in Single Shot Temporal Action Detection
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- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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