Papers › Towards High-Resolution Salient Object Detection
Towards High-Resolution Salient Object Detection
Yi Zeng, Pingping Zhang, Jianming Zhang, Zhe Lin, Huchuan Lu
Deep neural network based methods have made a significant breakthrough in salient object detection. However, they are typically limited to input images with low resolutions (400×400 pixels or less). Little effort has been made to train deep neural networks to directly handle salient object detection in very high-resolution images. This paper pushes forward high-resolution saliency detection, and contributes a new dataset, named High-Resolution Salient Object Detection (HRSOD). To our best knowledge, HRSOD is the first high-resolution saliency detection dataset to date. As another contribution, we also propose a novel approach, which incorporates both global semantic information and local high-resolution details, to address this challenging task. More specifically, our approach consists of a Global Semantic Network (GSN), a Local Refinement Network (LRN) and a Global-Local Fusion Network (GLFN). GSN extracts the global semantic information based on down-sampled entire image. Guided by the results of GSN, LRN focuses on some local regions and progressively produces high-resolution predictions. GLFN is further proposed to enforce spatial consistency and boost performance. Experiments illustrate that our method outperforms existing state-of-the-art methods on high-resolution saliency datasets by a large margin, and achieves comparable or even better performance than them on widely-used saliency benchmarks. The HRSOD dataset is available at https://github.com/yi94code/HRSOD.
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Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| RGB Salient Object Detection | DAVIS-S | Zeng et al. (HRSOD) | F-measure | 0.889 | #12 of 12 | Archive leaderboard | report |
| RGB Salient Object Detection | DAVIS-S | Zeng et al. (HRSOD) | MAE | 0.026 | #12 of 12 | Archive leaderboard | report |
| RGB Salient Object Detection | DAVIS-S | Zeng et al. (HRSOD) | S-measure | 0.876 | #12 of 12 | Archive leaderboard | report |
| RGB Salient Object Detection | DAVIS-S | Zeng et al. (HRSOD) | mBA | 0.618 | #12 of 12 | Archive leaderboard | report |
| RGB Salient Object Detection | HRSOD | Zeng et al. | MAE | 0.030 | #14 of 14 | Archive leaderboard | report |
| RGB Salient Object Detection | HRSOD | Zeng et al. | S-Measure | 0.892 | #14 of 14 | Archive leaderboard | report |
| RGB Salient Object Detection | HRSOD | Zeng et al. | mBA | 0.693 | #14 of 14 | Archive leaderboard | report |
| RGB Salient Object Detection | HRSOD | Zeng et al. | max F-Measure | 0.892 | #14 of 14 | Archive leaderboard | report |
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