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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I3D and R3D achieve better performance than vanilla ResNet50 and EfficientNet, but computation costs are huge.
“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]
Section 5.1 reports that Kinetics-pretrained I3D outperformed earlier methods on UCF-101 and HMDB-51, including a method using ResNet-50. It does not compare I3D with vanilla ResNet50 or EfficientNet, or establish the claimed computation costs; the R3D50 FLOPs figure is attached to a different citation in the citing sentence. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA 5-block non-local C2D with ResNet-101 outperforms inflated 3D ConvNet (I3D) on Kinetics.
“(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]
With a few non-local blocks, non-local neural networks are more accurate for video classification than 2D and 3D convolutional networks (including the inflated variant).
“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]
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.
“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
Compared to RNN-based architectures, Transformers have significant advantages in long-term dependency and parallel computation.
“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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“In our experiments, we use the I3D features without fine-tuning.”
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End-to-End Learning of Visual Representations From Uncurated Instructional Videos
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Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks
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Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00725 · Cited by 606 · see citing works
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630 · see citing works
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599 · see citing works
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W. H., & Carneiro, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00493 · Cited by 615 · see citing works
TEA: Temporal Excitation and Aggregation for Action Recognition
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What Makes Training Multi-modal Classification Networks Hard?
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Masked Feature Prediction for Self-Supervised Visual Pre-Training
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Neimark, D., Bar, O., Zohar, M., & Asselmann, D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00355 · Cited by 551 · see citing works
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision
Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., & Yang, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_20 · Cited by 521 · see citing works
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y., Wang, Y., Wang, Y., & Qiao, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01398 · Cited by 536 · see citing works
TDN: Temporal Difference Networks for Efficient Action Recognition
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G-TAD: Sub-Graph Localization for Temporal Action Detection
Xu, M., Zhao, C., Rojas, D. S., Thabet, A., & Ghanem, B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01017 · Cited by 503 · see citing works
A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503 · see citing works
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
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PoseTrack: A Benchmark for Human Pose Estimation and Tracking
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Temporal Pyramid Network for Action Recognition
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ActionFormer: Localizing Moments of Actions with Transformers
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Medical Image Data Augmentation: Techniques, Comparisons and Interpretations
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WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
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“[10] João Carreira and Andrew Zisserman.”
MethodMulti-modal Transformer for Video Retrieval
Gabeur, V., Sun, C., Alahari, K., & Schmid, C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_13 · Cited by 615 · see citing works
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Local Aggregation for Unsupervised Learning of Visual Embeddings
Zhuang, C., Zhai, A., & Yamins, D. L. K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00610 · Cited by 422 · see citing works
Self-Supervised Spatiotemporal Learning via Video Clip Order Prediction
Xu, D., Xiao, J., Zhao, Z., Shao, J., Xie, D., & Zhuang, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01058 · Cited by 417 · see citing works
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation
Niu, X., Shan, S., Han, H., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2947204 · Cited by 421 · see citing works
Prompting Visual-Language Models for Efficient Video Understanding
Ju, C., Han, T., Zheng, K., Zhang, Y., & Xie, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_7 · Cited by 423 · see citing works
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
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Drone-surveillance for search and rescue in natural disaster
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Appearance-and-Relation Networks for Video Classification
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Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
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VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and TextPreprint
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Negative Margin Matters: Understanding Margin in Few-Shot Classification
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MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
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TAM: Temporal Adaptive Module for Video Recognition
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Attentional ShapeContextNet for Point Cloud Recognition
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Compressed Video Action Recognition
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Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
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- 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 · 2016: 1 · 2012: 2
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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