The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Finding action tubes
Abstract
We address the problem of action detection in videos. Driven by the latest progress in object detection from 2D images, we build action models using rich feature hierarchies derived from shape and kinematic cues. We incorporate appearance and motion in two ways. First, starting from image region proposals we select those that are motion salient and thus are more likely to contain the action. This leads to a significant reduction in the number of regions being processed and allows for faster computations. Second, we extract spatio-temporal feature representations to build strong classifiers using Convolutional Neural Networks. We link our predictions to produce detections consistent in time, which we call action tubes. We show that our approach outperforms other techniques in the task of action detection.
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Cites
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Large-Scale Video Classification with Convolutional Neural Networks
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Cited by
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
Temporal Action Detection with Structured Segment Networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., & Lin, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.317 · Cited by 892
P-CNN: Pose-Based CNN Features for Action Recognition
Cheron, G., Laptev, I., & Schmid, C. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.368 · Cited by 633
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630
Self-citationDetect to Track and Track to Detect
Feichtenhofer, C., Pinz, A., & Zisserman, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.330 · Cited by 595
End-to-End Learning of Action Detection from Frame Glimpses in Videos
Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585
Cross Modal Distillation for Supervision Transfer
Gupta, S., Hoffman, J., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.309 · Cited by 530
“Note that in obtaining these performance improvements we are using exactly the same CNN architecture and amount of labeled data.”
MethodSelf-citationTURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
Gao, J., Yang, Z., Sun, C., Chen, K., & Nevatia, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.392 · Cited by 498
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Singh, B., Marks, T. K., Jones, M., Tuzel, O., & Shao, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.216 · Cited by 461
Object Detection from Video Tubelets with Convolutional Neural Networks
Kang, K., Ouyang, W., Li, H., & Wang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.95 · Cited by 386
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
RetinaTrack: Online Single Stage Joint Detection and Tracking
Lu, Z., Rathod, V., Votel, R., & Huang, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01468 · Cited by 254
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
Huang, D.-A., Fei-Fei, L., & Niebles, J. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_9 · Cited by 226
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Beery, S., Wu, G., Rathod, V., Votel, R., & Huang, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01309 · Cited by 140
STDnet-ST: Spatio-temporal ConvNet for small object detection
Bosquet, B., Mucientes, M., & Brea, V. M. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107929 · Cited by 57
“Several methods have been re-adapted from successful architectures in action detection [35, 36, 37].”
MethodActivity Driven Weakly Supervised Object Detection
Yang, Z., Mahajan, D., Ghadiyaram, D., Nevatia, R., & Ramanathan, V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00303 · Cited by 27
Metadata
- Authors
- Gkioxari, G., & Malik, J.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 759-768
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1923332106
- Cited by
- 528
- References
- 13
- FWCI
- 34.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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