Papers › Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
Youwei Pang, Lihe Zhang, Xiaoqi Zhao, Huchuan Lu
The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate dynamic filters with receptive fields of different sizes. In the end, we implement a kind of more flexible and efficient multi-scale cross-modal feature processing, i.e. dynamic dilated pyramid module. In order to make the predictions have sharper edges and consistent saliency regions, we design a hybrid enhanced loss function to further optimize the results. This loss function is also validated to be effective in the single-modal RGB SOD task. In terms of six metrics, the proposed method outperforms the existing twelve methods on eight challenging benchmark datasets. A large number of experiments verify the effectiveness of the proposed module and loss function. Our code, model and results are available at \url{https://github.com/lartpang/HDFNet}.
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Code
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Code Syntology ran Syntology
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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 | HDFNet | Average MAE | 0.037 | #7 of 27 | Archive leaderboard | report |
| RGB-D Salient Object Detection | NJU2K | HDFNet | S-Measure | 91.1 | #7 of 27 | Archive leaderboard | report |
| Thermal Image Segmentation | RGB-T-Glass-Segmentation | HDFNet | MAE | 0.048 | #9 of 22 | 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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