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Dynamic Equilibrium Module for Action Recognition
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AI
DOI：10.1109/ACCESS.2021.3135868.png)
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Abstract
En 中文
Temporal variations, such as sudden motion, acceleration and occlusions, occur frequently in real-world videos and force video-modeling networks to account for them. However. often they are not beneficial for recognizing actions at coarse granularity and thus may impede spatio-temporal learning. Prior solutions to this problem usually introduce multiple network branches to process input frames at different sampling rates or employ special components to explore inter-frame relations, which are computationally expensive. In this paper we propose a simple and flexible Dynamic Equilibrium Module (DEM) for video modeling through adaptive Eulerian motion manipulation. The proposed module can be directly inserted into 3D and (2+1)D backbone networks to effectively reduce the impact of temporal variations on video modeling and learn spatio-temporal representations with higher robustness. We demonstrate performance gains due to the use of DEM in R3D and R(2+1)D models on Kinetics-400, UCF-101, and HMDB-51 datasets.
Keywords:
Videos
Dynamic equilibrium
Three-dimensional displays
Sports
Optical flow
Optical filters
Dynamics
Action recognition
video analysis
deep learning