Saliency detection by multi-context deep learning
Chinese University of Hong Kong · Shenzhen Institutes of Advanced Technology · +1 more institution
Abstract
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. To provide a better initialization for training the deep neural networks, we investigate different pre-training…
Citation impact
- FWCI
- 65.84
- Percentile
- 100%
- References
- 87
Authors
4- RZRui ZhaoCorresponding
Chinese University of Hong Kong, Shenzhen Institutes of Advanced Technology
- WOWanli Ouyang
Chinese University of Hong Kong
- HLHongsheng Li
University of Electronic Science and Technology of China, Chinese University of Hong Kong
- XWXiaogang Wang
Shenzhen Institutes of Advanced Technology, Chinese University of Hong Kong
Topics & keywords
- Computer science
- Artificial intelligence
- Deep learning
- Initialization
- Object detection
- Convolutional neural network
- Context (archaeology)
- Machine learning