# SAM-helps-Shadow:When Segment Anything Model meet shadow removal

arXiv: https://arxiv.org/abs/2306.06113 · HTML: https://mlsanity.com/paper/2306.06113/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 47 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 27 of 27)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019)
- [Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data](https://mlsanity.com/paper/1903.10683/) (2019)
- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network](https://mlsanity.com/paper/2207.10434/) (2022)
- [From Shadow Segmentation to Shadow Removal](https://mlsanity.com/paper/2008.00267/) (2020)
- [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018)
- [From Shadow Generation to Shadow Removal](https://mlsanity.com/paper/2103.12997/) (2021)
- [CANet: A Context-Aware Network for Shadow Removal](https://mlsanity.com/paper/2108.09894/) (2021)
- [Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data](https://mlsanity.com/paper/2006.15617/) (2020)
- [SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model](https://mlsanity.com/paper/2305.02034/) (2023)
- [RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal](https://mlsanity.com/paper/1911.09178/) (2019)
- [Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network](https://mlsanity.com/paper/2009.13015/) (2020)
- [SiENet: Siamese Expansion Network for Image Extrapolation](https://mlsanity.com/paper/2007.03851/) (2020)
- [Unsupervised Portrait Shadow Removal via Generative Priors](https://mlsanity.com/paper/2108.03466/) (2021)
- [Segment Anything in Non-Euclidean Domains: Challenges and Opportunities](https://mlsanity.com/paper/2304.11595/) (2023)
- [Shadow-Aware Dynamic Convolution for Shadow Removal](https://mlsanity.com/paper/2205.04908/) (2022)
- [CRFormer: A Cross-Region Transformer for Shadow Removal](https://mlsanity.com/paper/2207.01600/) (2022)
- [From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection](https://mlsanity.com/paper/2304.03140/) (2023)
- [SpA-Former: Transformer image shadow detection and removal via spatial attention](https://mlsanity.com/paper/2206.10910/) (2022)
- [Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Network](https://mlsanity.com/paper/2010.01291/) (2020)
- [Self-Supervised Shadow Removal](https://mlsanity.com/paper/2010.11619/) (2020)
- [CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal](https://mlsanity.com/paper/2209.02174/) (2022)

## Built on by (most cited first, 6 of 6)

- [Moving Object Segmentation: All You Need Is SAM (and Flow)](https://mlsanity.com/paper/2404.12389/) (2024)
- [Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation](https://mlsanity.com/paper/2406.06163/) (2024)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [SAVE: Segment Audio-Visual Easy way using Segment Anything Model](https://mlsanity.com/paper/2407.02004/) (2024)
- [SoftShadow: Leveraging Soft Masks for Penumbra-Aware Shadow Removal](https://mlsanity.com/paper/2409.07041/) (2024)
- [SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model](https://mlsanity.com/paper/2410.01473/) (2024)

## Abstract (arXiv)

The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-shot learning in this field. In this study, we innovatively adapted the SAM (Segment anything model) for shadow removal by introducing SAM-helps-Shadow, effectively integrating shadow detection and removal into a single stage. Our approach utilized the model's detection results as a potent prior for facilitating shadow detection, followed by shadow removal using a second-order deep unfolding network. The source code of SAM-helps-Shadow can be obtained from https://github.com/zhangbaijin/SAM-helps-Shadow.