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TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
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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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
(2016) · Cited by 31,590
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
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- oneata, 2014, The lear submission at thumos 2014, ECCV workshop
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TALL: Temporal Activity Localization via Language Query
Gao, J., Sun, C., Yang, Z., & Nevatia, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.563 · Cited by 936
Self-citationHuman Action Recognition and Prediction: A Survey
Kong, Y., & Fu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01594-9 · Cited by 890
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Lin, T., Zhao, X., Su, H., Wang, C., & Yang, M. Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_1 · Cited by 816
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Chao, Y.-W., Vijayanarasimhan, S., Seybold, B., Ross, D. A., Deng, J., & Sukthankar, R. (2018) · 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/cvpr.2018.00124 · Cited by 751
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
Lin, T., Liu, X., Li, X., Ding, E., & Wen, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00399 · Cited by 694
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Farha, Y. A., & Gall, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00369 · Cited by 673
“The first stage is to generate proposals, and then classify and refine the boundaries of these proposals in the second stage [32], [33], [34], [35], [36], [37], [38], [39], [40].”
BackgroundEnd-to-End Dense Video Captioning with Masked Transformer
Zhou, L., Zhou, Y., Corso, J. J., Socher, R., & Xiong, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00911 · Cited by 605
Graph Convolutional Networks for Temporal Action Localization
Zeng, R., Huang, W., Gan, C., Tan, M., Rong, Y., Zhao, P., & Huang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00719 · Cited by 570
G-TAD: Sub-Graph Localization for Temporal Action Detection
Xu, M., Zhao, C., Rojas, D. S., Thabet, A., & Ghanem, 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/cvpr42600.2020.01017 · Cited by 519
RGB-D-based human motion recognition with deep learning: A survey
Wang, P., Li, W., Ogunbona, P., Wan, J., & Escalera, S. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.04.007 · Cited by 412
Gaussian Temporal Awareness Networks for Action Localization
Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., & Mei, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00043 · Cited by 380
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3021756 · Cited by 349
“The first stage is to generate proposals, and then classify and refine the boundaries of these proposals in the second stage [32], [33], [34], [35], [36], [37], [38], [39], [40].”
BackgroundYuan, Y., Mei, T., & Zhu, W. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019159 · Cited by 346
Attentive Moment Retrieval in Videos
Liu, M., Wang, X., Nie, L., He, X., Chen, B., & Chua, T. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210003 · Cited by 313
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Lin, C., Xu, C., Luo, D., Wang, Y., Tai, Y. Y., Wang, C., Li, J., Huang, F., & Fu, Y. (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/cvpr46437.2021.00333 · Cited by 304
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Liu, X., Wang, Q., Hu, Y., Tang, X., Zhang, S., Bai, S., & Bai, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3195321 · Cited by 316
AutoLoc: Weakly-Supervised Temporal Action Localization in Untrimmed Videos
Shou, Z., Gao, H., Zhang, L., Miyazawa, K., & Chang, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_10 · Cited by 289
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
Yang, A., Nagrani, A., Seo, P. H., Miech, A., Pont-Tuset, J., Laptev, I., Šivic, J., & Schmid, C. (2023) · 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/cvpr52729.2023.01032 · Cited by 296
Cross-modal Moment Localization in Videos
Liu, M., Wang, X., Nie, L., Tian, Q. C., Chen, B., & Chua, T. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240549 · Cited by 262
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Giancola, S., Amine, M., Dghaily, T., & Ghanem, B. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00223 · Cited by 289
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
Zhao, H., Torralba, A. B., Torresani, L., & Yan, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00876 · Cited by 283
Motion-Appearance Co-memory Networks for Video Question Answering
Gao, J., Ge, R., Chen, K., & Nevatia, R. (2018) · 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/cvpr.2018.00688 · Cited by 258
Self-citationBidirectional Attentive Fusion with Context Gating for Dense Video Captioning
Wang, J., Jiang, W., Ma, L., Liu, W., & Xu, Y. (2018) · 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/cvpr.2018.00751 · Cited by 237
SCSampler: Sampling Salient Clips From Video for Efficient Action Recognition
Korbar, B., Tran, D., & Torresani, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00633 · Cited by 227
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Liu, Y., Ma, L., Zhang, Y., Liu, W., & Chang, S. (2019) · 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/cvpr.2019.00372 · Cited by 222
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MAC: Mining Activity Concepts for Language-Based Temporal Localization
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ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
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FineAction: A Fine-Grained Video Dataset for Temporal Action Localization
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WSLLN:Weakly Supervised Natural Language Localization Networks
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DCAN: Improving Temporal Action Detection via Dual Context Aggregation
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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
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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
A Survey on Temporal Action Localization
Xia, H., & Zhan, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2986861 · Cited by 75
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 73
Activity Graph Transformer for Temporal Action LocalizationPreprint
Nawhal, M., & Mori, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.08540 · Cited by 89
GateHUB: Gated History Unit with Background Suppression for Online Action Detection
Chen, J., Mittal, G. K., Yu, Y., Kong, Y., & Chen, M. (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/cvpr52688.2022.01930 · Cited by 72
Cai, Z., Ghosh, S., Dhall, A., Gedeon, T., Stefanov, K., & Hayat, M. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103818 · Cited by 63
Modeling Multi-Label Action Dependencies for Temporal Action Localization
Tirupattur, P., Duarte, K., Rawat, Y. S., & Shah, M. A. (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/cvpr46437.2021.00151 · Cited by 63
Zero-Shot Temporal Action Detection via Vision-Language Prompting
Nag, S., Zhu, X., Song, Y.-Z., & Xiang, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_39 · Cited by 63
SSNet: Scale Selection Network for Online 3D Action Prediction
Liu, J., Shahroudy, A., Wang, G., Duan, L., & Kot, A. C. (2018) · 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/cvpr.2018.00871 · Cited by 62
Long-Term Video Question Answering via Multimodal Hierarchical Memory Attentive Networks
Yu, T., Yu, J., Zhou, Y., Huang, Q., & Tian, Q. C. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2995959 · Cited by 63
Gong, G., Zheng, L., & Mu, Y. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102850 · Cited by 60
StartNet: Online Detection of Action Start in Untrimmed Videos
Gao, M., Xu, M., Davis, L. S., Socher, R., & Xiong, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00564 · Cited by 65
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action LocalizationPreprint
Qu, S., Chen, G., Li, Z., Zhang, L., Fan, L., & Knoll, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02967 · Cited by 59
WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
Gao, M., Zhou, Y., Xu, R., Socher, R., & Xiong, C. (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/cvpr46437.2021.00195 · Cited by 58
Graph Attention Based Proposal 3D ConvNets for Action Detection
Li, J., Liu, X., Zong, Z., Zhao, W., Zhang, M., & Song, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5893 · Cited by 58
Video Summarization Via Actionness Ranking
Elfeki, M., & Borji, A. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00085 · Cited by 57
Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning
Nag, S., Zhu, X., Song, Y.-Z., & Xiang, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_37 · Cited by 56
Decoupling Localization and Classification in Single Shot Temporal Action Detection
Huang, Y., Dai, Q., & Lu, Y. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00224 · Cited by 55
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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