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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Open accessAbstract
The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-of-the-art architectures in light of the new Kinetics Human Action Video dataset. Kinetics has two orders of magnitude more data, with 400 human action classes and over 400 clips per class, and is collected from realistic, challenging YouTube videos. We provide an analysis on how current architectures fare on the task of action classification on this dataset and how much performance improves on the smaller benchmark datasets after pre-training on Kinetics. We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on 2D ConvNet inflation: filters and pooling kernels of very deep image classification ConvNets are expanded into 3D, making it possible to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and even their parameters. We show that, after pre-training on Kinetics, I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.2% on HMDB-51 and 97.9% on UCF-101.
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“Although I3D [4] and R3D [19] achieve better performance than vanilla ResNet50 [20] and EfficientNet [50], the computation costs are huge, such as R3D50 with 296G FLOPs.”
From TALL: Thumbnail Layout for Deepfake Video Detection (2023) · cites this paper as [4]
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“(e) Non-local vs . 3D Conv : A 5-block non-local C2D vs . inflated 3D ConvNet (I3D) [7]. All entries are with ResNet-101. The numbers of parameters and FLOPs are relative to the C2D baseline (43.2M and 34.2B).”
From Non-local Neural Networks (2018) · cites this paper as [7]
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“With a few non-local blocks, our architecures called non-local neural networks are more accurate for video classification than 2D and 3D convolutional networks [48] (including the inflated variant [7]).”
From Non-local Neural Networks (2018) · cites this paper as [7]
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“many of the deep-learning architectures mentioned in this review have similarities to ones used in analysis of digital media that try to combine multiple modalities to improve predictive performance of video content (Carreira and Zisserman, 2017; Afouras et al., 2019).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Carreira and Zisserman, 2017
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“Compared to RNN-based architectures, Transformers [137] have significant advantages in long-term dependency and parallel computation.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [137]
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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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Self-citationFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
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
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
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Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
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Parida, K. K., Matiyali, N., Guha, T., & Sharma, G. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093438 · Cited by 49
Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101973 · Cited by 48
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Geng, S., Gao, P., Chatterjee, M., Hori, C., Le Roux, J., Zhang, Y., Li, H., & Cherian, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16231 · Cited by 38
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Zhou, B., Wang, P., Wan, J., Liang, Y., & Wang, F. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3274783 · Cited by 38
Lee, J. T., Park, E., & Jung, T.-D. (2019) · Sensors · doi:10.3390/s19183873 · Cited by 30
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Bull, H., Afouras, T., Varol, G., Albanie, S., Momeni, L., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01135 · Cited by 30
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Yang, Z., Mahajan, D., Ghadiyaram, D., Nevatia, R., & Ramanathan, V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00303 · Cited by 27
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Pradhan, B. R., Bethi, Y., Narayanan, S., Chakraborty, A., & Thakur, C. S. (2019) · 2019 IEEE International Symposium on Circuits and Systems (ISCAS) · doi:10.1109/iscas.2019.8702581 · Cited by 31
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Coccomini, D. A., Zilos, G. K., Amato, G., Caldelli, R., Falchi, F., Papadopoulos, S., & Gennaro, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3409054 · Cited by 33
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Zhang, W., Xu, D., Zhang, J., & Ouyang, W. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3052083 · Cited by 25
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Zhang, Z., Zhou, Y., Li, C., Zhao, B., Liu, X., & Zhai, G. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3722559 · Cited by 23
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Rehman, A., & Belhaouari, S. B. (2021) · doi:10.36227/techrxiv.15172920 · Cited by 17
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Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2020) · arXiv · Cited by 350
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Kim, G., & Kim, E. (2022) · Electronics Letters · doi:10.1049/ell2.12678 · Cited by 13
Speech2Action: Cross-Modal Supervision for Action Recognition
Nagrani, A., Sun, C., Ross, D., Sukthankar, R., Schmid, C., & Zisserman, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01033 · Cited by 12
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You, Z., Wen, Z., Chen, Y., Li, X., Zeng, R., Wang, Y., & Tan, M. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3514820 · Cited by 11
Motion-driven adaptive frame selection strategy for video action recognition
Ding, H., Guo, C., Sun, J., Jiang, X., Shi, H., & Li, J. (2025) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-025-00675-2 · Cited by 9
Schut, E. H. S., Alonso, A., Smits, S., Khamassi, M., Samanta, A., Negwer, M., Kasri, N. N., Navarro Lobato, I., & Genzel, L. (2020) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2020.107265 · Cited by 8
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Feng, Y., Zhang, Z., Quan, R., Wang, L., & Qin, J. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3611872 · Cited by 8
Lee, J. T., & Park, E. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00919-9_38 · Cited by 7
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Palaskar, S., Sanabria, R., & Metze, F. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101093 · Cited by 6
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Almadani, A., Agu, E., Sarwar, A., Ahluwalia, M., & Kpodonu, J. (2023) · 2023 IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh60066.2023.00039 · Cited by 5
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Metadata
- Authors
- Carreira, J., & Zisserman, A.
- Affiliations
- University of Oxford (GB)
- ORCID
- Zisserman, Andrew
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4724-4733
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2619082050
- Cited by
- 9,570
- References
- 38
- Citations by year
- 2026: 702 · 2025: 1227 · 2024: 1428 · 2023: 1500 · 2022: 1315 · 2021: 1414 · 2020: 994 · 2019: 697 · 2018: 274 · 2017: 16
- FWCI
- 158.44
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (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 › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- human action recognition, I3D, dual-stream architecture, spatiotemporal feature extraction, pre-training, video classification, UCF101, HMDB51, YouTube videos
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