# SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection

arXiv: https://arxiv.org/abs/2308.08935 · HTML: https://mlsanity.com/paper/2308.08935/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 68 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019)
- [Rendering Synthetic Objects into Legacy Photographs](https://mlsanity.com/paper/1912.11565/) (2019)
- [Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2011.13144/) (2020)
- [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018)
- [RGB-D Salient Object Detection with Cross-Modality Modulation and Selection](https://mlsanity.com/paper/2007.07051/) (2020)
- [PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators](https://mlsanity.com/paper/2306.08918/) (2023)
- [DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2003.08608/) (2020)
- [A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation](https://mlsanity.com/paper/1712.01361/) (2017)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)
- [Instance Shadow Detection](https://mlsanity.com/paper/1911.07034/) (2019)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [Revisiting Shadow Detection: A New Benchmark Dataset for Complex World](https://mlsanity.com/paper/1911.06998/) (2019)
- [Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.01971/) (2021)
- [RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2110.14223/) (2021)
- [FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation](https://mlsanity.com/paper/2204.01587/) (2022)
- [Global-and-Local Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2204.08917/) (2022)
- [Triple-cooperative Video Shadow Detection](https://mlsanity.com/paper/2103.06533/) (2021)
- [A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels](https://mlsanity.com/paper/2209.02957/) (2022)
- [Unsupervised Portrait Shadow Removal via Generative Priors](https://mlsanity.com/paper/2108.03466/) (2021)
- [BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2207.08114/) (2022)
- [Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360deg Omnidirectional Image](https://mlsanity.com/paper/2212.12378/) (2022)
- [Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training](https://mlsanity.com/paper/2206.08801/) (2022)
- [PSNet: Parallel Symmetric Network for Video Salient Object Detection](https://mlsanity.com/paper/2210.05912/) (2022)
- [Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data](https://mlsanity.com/paper/2203.10474/) (2022)

## Built on by (most cited first, 11 of 11)

- [Language-Driven Interactive Shadow Detection](https://mlsanity.com/paper/2408.08543/) (2024)
- [Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era](https://mlsanity.com/paper/2409.02108/) (2024)
- [Timeline and Boundary Guided Diffusion Network for Video Shadow Detection](https://mlsanity.com/paper/2408.11785/) (2024)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [AdapterShadow: Adapting Segment Anything Model for Shadow Detection](https://mlsanity.com/paper/2311.08891/) (2023)
- [TS-SAM: Fine-Tuning Segment-Anything Model for Downstream Tasks](https://mlsanity.com/paper/2408.01835/) (2024)
- [LGTM: Training-Free Light-Guided Text-to-Image Diffusion Model via Initial Noise Manipulation](https://mlsanity.com/paper/2603.24086/) (2026)
- [RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification](https://mlsanity.com/paper/2408.17143/) (2024)
- [DTTNet: Improving Video Shadow Detection via Dark-Aware Guidance and Tokenized Temporal Modeling](https://mlsanity.com/paper/2511.06925/) (2025)
- [SARU: A Shadow-Aware and Removal Unified Framework for Remote Sensing Images with New Benchmarks](https://mlsanity.com/paper/2604.25432/) (2026)
- [Consistency as Regularization for Unsupervised Shadow Removal](https://mlsanity.com/paper/2609.01806/) (2026)

## Abstract (arXiv)

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.