The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization
Open accessAbstract
Temporal action localization is an important yet challenging task in video understanding. Typically, such a task aims at inferring both the action category and localization of the start and end frame for each action instance in a long, untrimmed video. While most current models achieve good results by using pre-defined anchors and numerous actionness, such methods could be bothered with both large number of outputs and heavy tuning of locations and sizes corresponding to different anchors. Instead, anchor-free methods is lighter, getting rid of redundant hyper-parameters, but gains few attention. In this paper, we propose the first purely anchor-free temporal localization method, which is both efficient and effective. Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module to gather more valuable boundary features for each proposal with a novel boundary pooling, and (iii) several consistency constraints to make sure our model can find the accurate boundary given arbitrary proposals. Extensive experiments show that our method beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3. Code is available at https://github.com/TencentYoutuResearch/ActionDetection-AFSD.
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Cites
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Focal Loss for Dense Object Detection
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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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FCOS: Fully Convolutional One-Stage Object Detection
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ActivityNet: A large-scale video benchmark for human activity understanding
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“We then assemble all predictions and process them with Soft-NMS [3] to suppress redundant proposals.”
MethodInfluentialTURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
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Cited by
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y., Wang, Y., Wang, Y., & Qiao, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01398 · Cited by 536
Self-citation(2023) · doi:10.1109/cvpr52729.2023.01808 · Cited by 226
UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization
Zhang, R., Wang, H., Du, M., Liu, H., Zhou, Y., & Zeng, Q. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3613767 · Cited by 42
Hear Me out: Fusional Approaches for Audio Augmented Temporal Action Localization
Bagchi, A., Mahmood, J., Fernandes, D., & Sarvadevabhatla, R. (2022) · Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0010832700003124 · Cited by 29
RefineTAD: Learning Proposal-free Refinement for Temporal Action Detection
Feng, Y., Zhang, Z., Quan, R., Wang, L., & Qin, J. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3611872 · Cited by 8
Metadata
- Authors
- Lin, C., Xu, C., Luo, D., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., & Fu, Y.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 3319-3328
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3176444885
- Cited by
- 289
- References
- 11
- FWCI
- 18.71
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lin, C., Xu, C., Luo, D., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., & Fu, Y. (2021). Learning Salient Boundary Feature for Anchor-free Temporal Action Localization. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00333
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