Papers › Attentive Feedback Network for Boundary-Aware Salient Object Detection
Attentive Feedback Network for Boundary-Aware Salient Object Detection
Mengyang Feng, Huchuan Lu, Errui Ding
Recent deep learning based salient object detection methods achieve gratifying performance built upon Fully Convolutional Neural Networks (FCNs). However, most of them have suffered from the boundary challenge. The state-of-the-art methods employ feature aggregation tech- nique and can precisely find out wherein the salient object, but they often fail to segment out the entire object with fine boundaries, especially those raised narrow stripes. So there is still a large room for improvement over the FCN based models. In this paper, we design the Attentive Feedback Modules (AFMs) to better explore the structure of objects. A Boundary-Enhanced Loss (BEL) is further employed for learning exquisite boundaries. Our proposed deep model produces satisfying results on the object boundaries and achieves state-of-the-art performance on five widely tested salient object detection benchmarks. The network is in a fully convolutional fashion running at a speed of 26 FPS and does not need any post-processing.
Code
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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 | SOC | AFNet | Average MAE | 0.115 | #6 of 7 | Archive leaderboard | report |
| RGB Salient Object Detection | SOC | AFNet | S-Measure | 0.700 | #6 of 7 | Archive leaderboard | report |
| RGB Salient Object Detection | SOC | AFNet | mean E-Measure | 0.684 | #6 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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