Temporal Relational Reasoning in Videos

Bolei Zhou Alex Andonian Aude Oliva Antonio Torralba
MIT Computer Science and Artificial Intelligence Laboratory


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Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. We introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.


Demo Video

Activity recognition for a long video performed by Bolei playing his hands. Model is trained on something-something dataset created by TwentyBN.

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Temporal Relation Networks

The framework of the temporal relation networks is as follows: To capture the multi-scale temporal relations inside a video, representative frames of the video are sampled and fed into different frame relation modules. Please refer to the paper for the detail.

Reference

B. Zhou, A. Andonian, A. Oliva, and A. Torralba. Temporal Relational Reasoning in Videos. European Conference on Computer Vision (ECCV), 2018. [Download Paper]

@article{zhou2017temporalrelation,
  title={Temporal Relational Reasoning in Videos},
  author={Zhou, Bolei and Andonian, Alex and Oliva, Aude and Torralba, Antonio},
  journal={European Conference on Computer Vision},
  year={2018}
}

Acknowledgement:
Project is supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/ Interior Business Center (DOI/IBC) contract number D17PC00344 and D17PC00341. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. BZ is supported by Facebook Fellowship.

Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/IBC, or the U.S.

Press Coverage

VentureBeat: MIT CSAIL designs AI that can track objects over time.

MIT News: Helping computers fill in the gaps between video frames.