Papers › Deeply supervised salient object detection with short connections
Deeply supervised salient object detection with short connections
Qibin Hou, Ming-Ming Cheng, Xiao-Wei Hu, Ali Borji, Zhuowen Tu, Philip Torr
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and saliency 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 salience detection is not obvious. In this paper, we propose a new method for saliency detection by introducing short connections to the skip-layer structures within the HED architecture. Our framework provides rich multi-scale feature maps 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 (0.15 seconds per image), effectiveness, and simplicity over the existing algorithms.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| RGB Salient Object Detection | DUTS-TE | DSS | MAE | 0.065 | #25 of 31 | Archive leaderboard | report |
| RGB Salient Object Detection | DUTS-TE | DSS | max F-measure | 0.813 | #25 of 31 | Archive leaderboard | report |
| RGB Salient Object Detection | ISTD | DSS | Balanced Error Rate | 10.48 | #7 of 7 | Archive leaderboard | report |
| RGB Salient Object Detection | SBU / SBU-Refine | DSS | Balanced Error Rate | 7.00 | #4 of 7 | Archive leaderboard | report |
| RGB Salient Object Detection | UCF | DSS | Balanced Error Rate | 10.56 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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