Graph Distillation for Action Detection with Privileged Modalities
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In action recognition, prior missing modality studies follow transfer learning via knowledge distillation.
“In the context of action recognition (Tran et al. 2015; Wang et al. 2016; Carreira and Zisserman 2017; Feichtenhofer et al. 2019), there are handful of studies towards this end (Hoffman, Gupta, and Darrell 2016; Luo et al. 2018; Garcia, Morerio, and Murino 2018, 2019; Garcia et al. 2019; Stroud et al. 2020), all of which follow transfer learning scheme via knowledge distillation (Hinton et al. 201”
From Towards Good Practices for Missing Modality Robust Action Recognition (2023) · cites this paper as Luo et al. 2018
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works
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 · see citing works
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works
“Since it is nontrivial to jointly train on multiple modalities from scratch, we employ curriculum learning [1] to train the distillation graph.”
MethodA Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658 · cited 1× · see citing works
“The optical flow is calculated on the RGB videos using the dual TV-L1 method [62].”
MethodCaruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577 · see citing works
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 · cited 5× · see citing works
“[15] employed the “soft logits” obtained from different layers of the labeled modality.”
BackgroundMethodInfluentialRGBD-HuDaAct: A color-depth video database for human daily activity recognition
Ni, B., Wang, G., & Moulin, P. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130379 · Cited by 304 · see citing works
End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos
Buch, S., Escorcia, V., Ghanem, B., & Niebles, J. C. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.93 · Cited by 240 · cited 1× · see citing works
“kled the temporal action detection task. Previous work includes frame-level or sliding window classification methods [14,19,36], proposalbased methods [11,49,69], and end-to-end single-pass frameworks [63,2]. The success in RGB video understanding has given rise to a series of studies on action recognition with differe…”
MethodSelf-citationLearning with Side Information through Modality Hallucination
Hoffman, J., Gupta, S., & Darrell, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.96 · Cited by 207 · see citing works
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Knowledge Distillation: A Survey
Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931 · see citing works
Decoupled Multimodal Distilling for Emotion Recognition
Li, Y., Wang, Y., & Cui, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00641 · Cited by 249 · see citing works
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation
Reza, M. K., Prater-Bennette, A., & Asif, M. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3476487 · Cited by 61 · see citing works
“We have shown mean accuracy, F1 score and % mIoU for each experiment.”
MethodTowards Good Practices for Missing Modality Robust Action Recognition
Woo, S., Lee, S., Park, Y., Nugroho, M. A., & Kim, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25378 · Cited by 60 · see citing works
- Authors
- Luo, Z., Hsieh, J.-T., Jiang, L., Niebles, J. C., & Fei-Fei, L.
- Affiliations
- Google (United States) (US)
- Stanford University (US)
- Corresponding author
- Luo, Zelun
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 174-192
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2883630736
- Cited by
- 111
- References
- 12
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 17 · 2023: 18 · 2022: 12 · 2021: 21 · 2020: 17 · 2019: 12 · 2018: 2
- FWCI
- 3.92
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- action detection, privileged information, limited training data, modality discrepancy, cross-domain transfer learning, action classification, 3D human action recognition
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