Saliency Unified: A Deep Architecture For Simultaneous Eye Fixation Prediction And Salient Object Segmentation
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016)
Abstract
Human eye fixations often correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address the related aspects of eye fixations and object saliency. In this work, we propose a deep convolutional neural network (CNN) capable of predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the object level semantics and the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. In addition, our network captures saliency at multiple scales via inception-style convolution blocks. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.
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Key words
simultaneous human eye fixation prediction,salient object segmentation,deep convolutional neural network,deep CNN,object level semantics,object saliency global contextual aspects,inception-style convolution blocks
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