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arXiv · 1912.01601

LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition

Zuxuan Wu·Caiming Xiong·Yu-Gang Jiang·Larry S. Davis
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Abstract

This paper presents LiteEval, a simple yet effective coarse-to-fine framework for resource efficient video recognition, suitable for both online and offline scenarios. Exploiting decent yet computationally efficient features derived at a coarse scale with a lightweight CNN model, LiteEval dynamically decides on-the-fly whether to compute more powerful features for incoming video frames at a finer scale to obtain more details. This is achieved by a coarse LSTM and a fine LSTM operating cooperatively, as well as a conditional gating module to learn when to allocate more computation. Extensive experiments are conducted on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate LiteEval requires substantially less computation while offering excellent classification accuracy for both online and offline predictions.

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Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang, Larry S. Davis. 2019-12-03. LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition. https://arxiv.org/abs/1912.01601

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