Authors:
Xuan Wang
1
;
Hao Tang
1
;
2
and
Zhigang Zhu
1
;
3
Affiliations:
1
The Graduate Center - CUNY, New York, NY 10016, U.S.A.
;
2
Borough of Manhattan Community College - CUNY, New York, NY 10007, U.S.A.
;
3
The City College of New York - CUNY, New York, NY 10031, U.S.A.
Keyword(s):
Deep Learning, Context Understanding, Convolutional Neural Networks, Graph Convolutional Network.
Abstract:
Contextual information has been widely used in many computer vision tasks. However, existing approaches design specific contextual information mechanisms for different tasks. In this work, we propose a general context learning and reasoning framework for object detection tasks with three components: local contextual labeling, contextual graph generation and spatial contextual reasoning. With simple user defined parameters, local contextual labeling automatically enlarge the small object labels to include more local contextual information. A Graph Convolutional Network learns over the generated contextual graph to build a semantic space. A general spatial relation is used in spatial contextual reasoning to optimize the detection results. All three components can be easily added and removed from a standard object detector. In addition, our approach also automates the training process to find the optimal combinations of user defined parameters. The general framework can be easily adapte
d to different tasks. In this paper we compare our framework with a previous multistage context learning framework specifically designed for storefront accessibility detection and a state of the art detector for pedestrian detection. Experimental results on two urban scene datasets demonstrate that our proposed general framework can achieve same performance as the specifically designed multistage framework on storefront accessibility detection, and with improved performance on pedestrian detection over the state of art detector.
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