Papers › SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Runmin Cong, Yuchen Guan, Jinpeng Chen, Wei zhang, Yao Zhao, Sam Kwong
Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on complex backgrounds. Drawing inspiration from the human visual system, we treat the input shadow image as a composition of a background layer and a shadow layer, and design a Style-guided Dual-layer Disentanglement Network (SDDNet) to model these layers independently. To achieve this, we devise a Feature Separation and Recombination (FSR) module that decomposes multi-level features into shadow-related and background-related components by offering specialized supervision for each component, while preserving information integrity and avoiding redundancy through the reconstruction constraint. Moreover, we propose a Shadow Style Filter (SSF) module to guide the feature disentanglement by focusing on style differentiation and uniformization. With these two modules and our overall pipeline, our model effectively minimizes the detrimental effects of background color, yielding superior performance on three public datasets with a real-time inference speed of 32 FPS.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Shadow Detection | CUHK-Shadow | SDDNet (MM 2023) (512x512) | BER | 7.65 | #2 of 16 | Archive leaderboard | report |
| Shadow Detection | CUHK-Shadow | SDDNet (MM 2023) (256x256) | BER | 8.66 | #8 of 16 | Archive leaderboard | report |
| Shadow Detection | SBU / SBU-Refine | SDDNet (MM 2023) (512x512) | BER | 4.86 | #1 of 16 | Archive leaderboard | report |
| Shadow Detection | SBU / SBU-Refine | SDDNet (MM 2023) (256x256) | BER | 5.39 | #4 of 16 | 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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