Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection

@article{Najibi2018TowardsTS,
  title={Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection},
  author={Mahyar Najibi and Fan Yang and Qiaosong Wang and Robinson Piramuthu},
  journal={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2018},
  pages={1432-1441}
}
In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks. Instead of generating thousands of candidate bounding boxes and refining them, our network directly learns to generate the saliency map containing the exact number of salient objects. During training, we convert the ground-truth rectangular boxes to Gaussian distributions that better capture the ROI regarding individual salient objects… Expand
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