# Triple-cooperative Video Shadow Detection

arXiv: https://arxiv.org/abs/2103.06533 · HTML: https://mlsanity.com/paper/2103.06533/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Deeply supervised salient object detection with short connections](https://mlsanity.com/paper/1611.04849/) (2016)
- [FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation](https://mlsanity.com/paper/1902.09513/) (2019)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks](https://mlsanity.com/paper/2001.06810/) (2020)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation](https://mlsanity.com/paper/1912.04573/) (2019)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [Motion Guided Attention for Video Salient Object Detection](https://mlsanity.com/paper/1909.07061/) (2019)
- [Rendering Synthetic Objects into Legacy Photographs](https://mlsanity.com/paper/1912.11565/) (2019)
- [Review of Visual Saliency Detection with Comprehensive Information](https://mlsanity.com/paper/1803.03391/) (2018)
- [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018)
- [Direction-aware Spatial Context Features for Shadow Detection](https://mlsanity.com/paper/1712.04142/) (2017)
- [Video Salient Object Detection via Fully Convolutional Networks](https://mlsanity.com/paper/1702.00871/) (2017)
- [A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation](https://mlsanity.com/paper/1712.01361/) (2017)
- [Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network](https://mlsanity.com/paper/1709.09283/) (2017)

## Built on by (most cited first, 22 of 22)

- [Instance Shadow Detection with A Single-Stage Detector](https://mlsanity.com/paper/2207.04614/) (2022)
- [VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection](https://mlsanity.com/paper/2108.08482/) (2021)
- [SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3](https://mlsanity.com/paper/2509.00833/) (2025)
- [SCOTCH and SODA: A Transformer Video Shadow Detection Framework](https://mlsanity.com/paper/2211.06885/) (2022)
- [Detect Any Shadow: Segment Anything for Video Shadow Detection](https://mlsanity.com/paper/2305.16698/) (2023)
- [Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training](https://mlsanity.com/paper/2206.08801/) (2022)
- [SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection](https://mlsanity.com/paper/2308.08935/) (2023)
- [Fine-Context Shadow Detection using Shadow Removal](https://mlsanity.com/paper/2109.09609/) (2021)
- [Learning Shadow Correspondence for Video Shadow Detection](https://mlsanity.com/paper/2208.00150/) (2022)
- [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)
- [Temporal Feature Warping for Video Shadow Detection](https://mlsanity.com/paper/2107.14287/) (2021)
- [AdapterShadow: Adapting Segment Anything Model for Shadow Detection](https://mlsanity.com/paper/2311.08891/) (2023)
- [Segment Anything for Videos: A Systematic Survey](https://mlsanity.com/paper/2408.08315/) (2024)
- [Video Instance Shadow Detection Under the Sun and Sky](https://mlsanity.com/paper/2211.12827/) (2022)
- [Learning Physical-Spatio-Temporal Features for Video Shadow Removal](https://mlsanity.com/paper/2303.09370/) (2023)
- [Fusion of Short-term and Long-term Attention for Video Mirror Detection](https://mlsanity.com/paper/2407.07999/) (2024)
- [MirrorSAM2: Segment Mirror in Videos with Depth Perception](https://mlsanity.com/paper/2509.17220/) (2025)
- [DTTNet: Improving Video Shadow Detection via Dark-Aware Guidance and Tokenized Temporal Modeling](https://mlsanity.com/paper/2511.06925/) (2025)
- [Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning](https://mlsanity.com/paper/2512.06179/) (2025)
- [After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data](https://mlsanity.com/paper/2609.38607/) (2026)

## Abstract (arXiv)

Shadow detection in a single image has received significant research interest in recent years. However, much fewer works have been explored in shadow detection over dynamic scenes. The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection. In this work, we collect a new video shadow detection dataset, which contains 120 videos with 11, 685 frames, covering 60 object categories, varying lengths, and different motion/lighting conditions. All the frames are annotated with a high-quality pixel-level shadow mask. To the best of our knowledge, this is the first learning-oriented dataset for video shadow detection. Furthermore, we develop a new baseline model, named triple-cooperative video shadow detection network (TVSD-Net). It utilizes triple parallel networks in a cooperative manner to learn discriminative representations at intra-video and inter-video levels. Within the network, a dual gated co-attention module is proposed to constrain features from neighboring frames in the same video, while an auxiliary similarity loss is introduced to mine semantic information between different videos. Finally, we conduct a comprehensive study on ViSha, evaluating 12 state-of-the-art models (including single image shadow detectors, video object segmentation, and saliency detection methods). Experiments demonstrate that our model outperforms SOTA competitors.