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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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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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