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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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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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