Papers › Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection
Yongri Piao, Wei Ji, Jingjing Li, Miao Zhang, Huchuan Lu
In this work, we propose a novel depth-induced multi-scale recurrent attention network for saliency detection. It achieves dramatic performance especially in complex scenarios. There are three main contributions of our network that are experimentally demonstrated to have significant practical merits. First, we design an effective depth refinement block using residual connections to fully extract and fuse multi-level paired complementary cues from RGB and depth streams. Second, depth cues with abundant spatial information are innovatively combined with multi-scale context features for accurately locating salient objects. Third, we boost our model's performance by a novel recurrent attention module inspired by Internal Generative Mechanism of human brain. This module can generate more accurate saliency results via comprehensively learning the internal semantic relation of the fused feature and progressively optimizing local details with memory-oriented scene understanding. In addition, we create a large scale RGB-D dataset containing more complex scenarios, which can contribute to comprehensively evaluating saliency models. Extensive experiments on six public datasets and ours demonstrate that our method can accurately identify salient objects and achieve consistently superior performance over 16 state-of-the-art RGB and RGB-D approaches.
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-D Salient Object Detection | NJU2K | DMRA | Average MAE | 0.051 | #22 of 27 | Archive leaderboard | report |
| RGB-D Salient Object Detection | NJU2K | DMRA | S-Measure | 88.6 | #22 of 27 | Archive leaderboard | report |
| RGB-D Salient Object Detection | NJU2K | DMRA | max E-Measure | 92.7 | #22 of 27 | Archive leaderboard | report |
| RGB-D Salient Object Detection | NJU2K | DMRA | max F-Measure | 88.6 | #22 of 27 | 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.
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