Connectionist Temporal Modeling for Weakly Supervised Action Labeling
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Recurrent models are difficult to interpret.
“recurrent models [27, 9], which can capture latent temporal patterns but are difficult to interpret”
From Temporal Convolutional Networks for Action Segmentation and Detection (2017) · cites this paper as [9]
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- Authors
- Huang, D.-A., Fei-Fei, L., & Niebles, J. C.
- Affiliations
- Stanford University (US)
- Corresponding author
- Huang, De-An
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 137-153
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2477205648
- Cited by
- 226
- References
- 50
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 15 · 2023: 22 · 2022: 32 · 2021: 38 · 2020: 33 · 2019: 28 · 2018: 26 · 2017: 16 · 2016: 3
- FWCI
- 8.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (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 › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- video action recognition, connectionist temporal classification, dynamic programming, semi-supervised learning, temporal alignment
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