Dynamic Inference: A New Approach Toward Efficient Video Action Recognition

By Wenhao Wu, Dongliang He, Xiao Tan, Shifeng Chen, Yi Yang, Shilei Wen. 2020

37 citations recorded by the source corpus.

Though action recognition in videos has achieved great success recently, it remains a challenging task due to the massive computational cost. Designing lightweight networks is a possible solution, but it may degrade the recognition performance. In this paper, we innovatively propose a general dynamic inference idea to improve inference efficiency by leveraging the variation in the distinguishability of different videos. The dynamic inference approach can be achieved from aspects of the network depth and the number of input video frames, or even in a joint input-wise and network depth-wise manner. In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module. The inference is carried out progressively on the grid by following some predefined route, whenever the inference process comes across a checkpoint, an early prediction can be made depending on whether the early stop criteria meets. For the proof-of-concept purpose, we instantiate several dynamic inference frameworks. In these instances, we overcome the drawback of limited temporal coverage resulted from an early prediction by a novel frame permutation scheme, and alleviate the conflict between progressive computation and video temporal relation modeling by introducing the online temporal shift module. Extensive experiments are conducted to thoroughly analyze the effectiveness of our ideas and to inspire future research efforts. Results on various datasets also evident the superiority of our approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism

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Citations in the corpus

MoViNets: Mobile Video Networks for Efficient Video Recognition (2021, background): “to improve 2D CNNs’ accuracy using temporal aggregation [40, 17, 70, 43, 16], however their limited inter-frame interactions reduce these models’ abilities to adequately model long-range temporal dependencies like 3D CNNs.”
Adaptive Focus for Efficient Video Recognition (2021, background): “, terminating the computation before “watching” the full sequence [10, 43]; (2) conditional computing, e.”
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition (2021, background): “Several works have been proposed that add decision branches to different layers of CNNs to learn whether to exit the network for faster inference (Figurnov et al., 2017; McGill & Perona, 2017; Wu et al., 2020).”