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Poster
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
Zuxuan Wu · Caiming Xiong · Yu-Gang Jiang · Larry Davis

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #175
in Applications -- Video Analysis »

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.

Keywords: Efficient Inference Methods · Video Analysis · Applications · Applications -> Computer Vision; Deep Learning
[ Paper]  [ Poster] 

Author Information

Zuxuan Wu (University of Maryland)
Caiming Xiong (Salesforce)
Yu-Gang Jiang (Fudan University)
Larry Davis (University of Maryland)

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