Multi-context deep learning framework for saliency detection

Year: 2,015
Authors: Rui Zhao, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
Journal:  IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Programming languages: C++, Cuda, Makerfile, Matlab, Protocol Buffeer, Python

Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework.

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