Qibin Hou¹ Ming-Ming Cheng¹  Xiaowei Hu¹  Ali Borji²  Zhuowen Tu³ Philip H. S. Torr⁴

¹CCCE, Nankai University      ²CRCV, UCF     ³UCSD     ⁴The University of Oxford

Online Demo

Abstract

Recent progress on salient object detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and salient object detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks (FCNs). There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. Holistically-Nested Edge Detector (HED) provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on saliency detection is not obvious. In this paper, we propose a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture. Our framework takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.08 seconds per image), effectiveness, and simplicity over the existing algorithms. Beyond that, we conduct an exhaustive analysis of the role of training data on performance. Our experimental results provide a more reasonable and powerful training set for future research and fair comparisons.

Paper

• Deeply supervised salient object detection with short connections, Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, Philip Torr, IEEE TPAMI (CVPR 2017), 2019.  [pdf] [Project Page] [bib] [source code & data] [official version] [poster] [中文版海报][LaTeX]

Source Code

You can find our code here. We have uploaded the caffe and CRF packages we used in our paper.

If you find our work is helpful, please cite

@article{HouPami19Dss,
  title={Deeply supervised salient object detection with short connections},
  author={Hou, Qibin and Cheng, Ming-Ming and Hu, Xiaowei and Borji, Ali and Tu, Zhuowen and Torr, Philip},
  year  = {2019},
  volume={41}, 
  number={4}, 
  pages={815-828}, 
  journal= {IEEE TPAMI},
  doi = {10.1109/TPAMI.2018.2815688},
}

Contact

andrewhoux AT gmail DOT com

Applications

This algorithm is used in flagship products such as Huawei Mate 10, Huawei Honour V10 etc, to create “AI Selfie: Brilliant Bokeh, perfect portraits”  effects as demonstrated in Mate 10 launch show, in Munich, Germany.

A report in Nature: link.