The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Rank Pooling for Action Recognition
Open accessAbstract
We propose a function-based temporal pooling method that captures the latent structure of the video sequence data - e.g., how frame-level features evolve over time in a video. We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation. As a specific example, we learn a pooling function via ranking machines. By learning to rank the frame-level features of a video in chronological order, we obtain a new representation that captures the video-wide temporal dynamics of a video, suitable for action recognition. Other than ranking functions, we explore different parametric models that could also explain the temporal changes in videos. The proposed functional pooling methods, and rank pooling in particular, is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions. We evaluate our method on various benchmarks for generic action, fine-grained action and gesture recognition. Results show that rank pooling brings an absolute improvement of 7-10 average pooling baseline. At the same time, rank pooling is compatible with and complementary to several appearance and local motion based methods and features, such as improved trajectories and deep learning features.
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Cites
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Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
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Cited by
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
Self-Supervised Video Representation Learning with Odd-One-Out Networks
Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.607 · Cited by 421
Self-citation
Metadata
- Authors
- Fernando, B., Gavves, E., Oramas M., J., Ghodrati, A., & Tuytelaars, T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-04-01
- Volume
- 39
- Issue
- 4
- Pages
- 773-787
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2193384753
- Cited by
- 304
- References
- 76
- FWCI
- 33.09
- Type
- journal article
- Language
- en
- Open access
- green
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