Fast Video Classification via Adaptive Cascading of Deep Models

Autor:	Shen, Haichen, Han, Seungyeop, Philipose, Matthai, Krishnamurthy, Arvind
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy. Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1611.06453 Zobrazit plný text záznamu View this record from Arxiv