Papers › ConvNet Architecture Search for Spatiotemporal Feature Learning

ConvNet Architecture Search for Spatiotemporal Feature Learning

16 Aug 2017arXiv:1708.05038archive 2025-07-28

Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, Manohar Paluri

Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning. Although any image representation can be applied to video frames, a dedicated spatiotemporal representation is still vital in order to incorporate motion patterns that cannot be captured by appearance based models alone. This paper presents an empirical ConvNet architecture search for spatiotemporal feature learning, culminating in a deep 3-dimensional (3D) Residual ConvNet. Our proposed architecture outperforms C3D by a good margin on Sports-1M, UCF101, HMDB51, THUMOS14, and ASLAN while being 2 times faster at inference time, 2 times smaller in model size, and having a more compact representation.

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Code

farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models mentioned on GitHubpytorch report

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Tasks

Action Classification Action Recognition Image Captioning Neural Architecture Search Object Detection Semantic Segmentation object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Action Classification	Kinetics-400	TSN	Acc@1	73.9	#166 of 207	Archive leaderboard	report
Action Recognition	HMDB-51	Res3D	Average accuracy of 3 splits	54.9	#71 of 77	Archive leaderboard	report
Action Recognition	UCF101	Res3D	3-fold Accuracy	85.8	#79 of 91	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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