SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization

Semi-supervised learning literature survey

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An image is worth 16x16 words: Transformers for image recognition at scale

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UCF101: A dataset of 101 human actions classes from videos in the wild

Original

Soomro, K. 2012 · 2012

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Imagenet large scale visual recognition challenge

Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015

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Temporal segment networks: Towards good practices for deep action recognition

Wang, L.; Xiong, Y.; Wang, Z.; Qiao, Y.; Lin, D.; Tang, X.; and Van Gool, L. 2016 · 2016

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Quo vadis, action recognition? a new model and the kinetics dataset

Carreira, J.; and Zisserman, A. 2017 · 2017

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Improved Regularization of Convolutional Neural Networks with Cutout

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DeVries, T. 2017 · 2017

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The” something something” video database for learning and evaluating visual common sense

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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Tarvainen, A.; and Valpola, H. 2017 · 2017

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Find and focus: Retrieve and localize video events with natural language queries

Shao, D.; Xiong, Y.; Zhao, Y.; Huang, Q.; Qiao, Y.; and Lin, D. 2018 · 2018

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Temporal segment networks for action recognition in videos

Wang, L.; Xiong, Y.; Wang, Z.; Qiao, Y.; Lin, D.; Tang, X.; and Van Gool, L. 2018 · 2018

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Temporal relational reasoning in videos

Zhou, B.; Andonian, A.; Oliva, A.; and Torralba, A. 2018 · 2018

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Learning motion in feature space: Locally-consistent deformable convolution networks for fine-grained action detection

Mac, K.-N. C.; Joshi, D.; Yeh, R. A.; Xiong, J.; Feris, R. S.; and Do, M. N. 2019 · 2019

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Cutmix: Regularization strategy to train strong classifiers with localizable features

Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 2019

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Randaugment: Practical automated data augmentation with a reduced search space

Cubuk, E. D.; Zoph, B.; Shlens, J.; and Le, Q. V. 2020 · 2020

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Memory-augmented dense predictive coding for video representation learning

Han, T.; Xie, W.; and Zisserman, A. 2020 · 2020

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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring

Kurakin, A.; Raffel, C.; Berthelot, D.; Cubuk, E. D.; Zhang, H.; Sohn, K.; and Carlini, N. 2020 · 2020

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Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Sohn, K.; Berthelot, D.; Carlini, N.; Zhang, Z.; Zhang, H.; Raffel, C. A.; Cubuk, E. D.; Kurakin, A.; and Li, C.-L. 2020 · 2020

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Unsupervised data augmentation for consistency training

Xie, Q.; Dai, Z.; Hovy, E.; Luong, T.; and Le, Q. 2020 · 2020

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Temporal pyramid network for action recognition

Yang, C.; Xu, Y.; Shi, J.; Dai, B.; and Zhou, B. 2020 · 2020

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Is space-time attention all you need for video understanding?

Bertasius, G.; Wang, H.; and Torresani, L. 2021 · 2021

Cited alongside, same era.

Video pose distillation for few-shot, fine-grained sports action recognition

Hong, J.; Fisher, M.; Gharbi, M.; and Fatahalian, K. 2021 · 2021

Cited alongside, same era.

Videossl: Semi-supervised learning for video classification

Jing, L.; Parag, T.; Wu, Z.; Tian, Y.; and Wang, H. 2021 · 2021

Cited alongside, same era.

Joint learning on the hierarchy representation for fine-grained human action recognition

Leong, M. C.; Tan, H. L.; Zhang, H.; Li, L.; Lin, F.; and Lim, J. H. 2021 · 2021

Cited alongside, same era.

In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning

M3net: multi-view encoding, matching, and fusion for few-shot fine-grained action recognition

Tang, H.; Liu, J.; Yan, S.; Yan, R.; Li, Z.; and Tang, J. 2023 · 2023

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Semi-Supervised Action Recognition From Temporal Augmentation Using Curriculum Learning

Tong, A.; Tang, C.; and Wang, W. 2023 · 2023

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Svformer: Semi-supervised video transformer for action recognition

Xing, Z.; Dai, Q.; Hu, H.; Chen, J.; Wu, Z.; and Jiang, Y.-G. 2023 · 2023

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Videoglue: Video general understanding evaluation of foundation models

Original

Yuan, L.; Gundavarapu, N. B.; Zhao, L.; Zhou, H.; Cui, Y.; Jiang, L.; Yang, X.; Jia, M.; Weyand, T.; Friedman, L.; et al. 2023 · 2023

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Learning representational invariances for data-efficient action recognition

Zou, Y.; Choi, J.; Wang, Q.; and Huang, J.-B. 2023 · 2023

Later among the works it cites.