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A key problem in salient object detection is how to effectively exploit the multi-level saliency cues in a unified and data-driven manner. In this paper, building upon the recent success of deep neural networks, we propose a fully convolutional neural network based approach empowered with multi-level fusion to salient object detection. By integrating saliency cues at different levels through fully...
This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative features for recognition. First, different viewpoints are mimicked by rotating the...
Hyperspectral image (HIS) classification is a hot topic in remote sensing community and most of the existing methods extract the features of original Hyperspectral data using shallow layer networks such as neural network (NN) and support vector machine (SVM). As deep learning recently achieves great success in machine learning and pattern recognition area for its ability in deep feature extraction...
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