Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
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Deep learning techniques may be applicable to problem domains where acquiring annotated data is costly, such as supervised activity recognition.
“This seems to indicate that although deep learning techniques are often employed with large amounts of data, (e.g., millions of frames in computer vision [22]), they may actually be applicable to problem domains where acquiring annotated data is very costly, such as in supervised activity recognition.”
From Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016) · cites this paper as [22]
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- Authors
- Pigou, L., van den Oord, A., Dieleman, S., Van Herreweghe, M., & Dambre, J.
- Affiliations
- Ghent University (BE)
- Corresponding author
- Pigou, Lionel
- Venue
- International Journal of Computer Vision
- Published
- 2016-10-04
- Volume
- 126
- Issue
- 2-4
- Pages
- 430-439
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W633646897
- Cited by
- 290
- References
- 31
- Citations by year
- 2026: 16 · 2025: 17 · 2024: 16 · 2023: 25 · 2022: 39 · 2021: 30 · 2020: 39 · 2019: 46 · 2018: 26 · 2017: 23 · 2016: 11 · 2012: 2
- FWCI
- 49.45
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Journal of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agentschap voor Innovatie door Wetenschap en Technologie (B/14438/23)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- gesture recognition, video, temporal pooling, temporal convolution, bidirectional recurrent neural network, recurrent neural networks, temporal structure
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