Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

@article{Hu2021RevisitingSD,
  title={Revisiting Shadow Detection: A New Benchmark Dataset for Complex World},
  author={Xiaowei Hu and Tianyu Wang and Chi-Wing Fu and Yitong Jiang and Qiong Wang and Pheng-Ann Heng},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  volume={30},
  pages={1925-1934}
}
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. Our dataset covers… 
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Citation Type

Learning From Synthetic Shadows for Shadow Detection and Removal
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It is demonstrated that shadow removal models trained on SynShadow perform well in removing shadows with diverse shapes and intensities on some challenging benchmarks, and it is shown that merely fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models.
Instance Shadow Detection
TLDR
LISA is designed, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction, and demonstrates its applicability on light direction estimation and photo editing.
From Shadow Segmentation to Shadow Removal
TLDR
This work proposes a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves, trained via an adversarial framework, following a physical model of shadow formation.
Pyramid Spatial Context Features for Salient Object Detection
  • Hui Li
  • Computer Science
    IEEE Access
  • 2020
TLDR
A novel deep neural network design for salient object detection by formulating a pyramid spatial context module, PSC module for short, to capture the spatial context information at multiple scales by inserting this module in a deep network, namely PSCNet.
Shadow Detection via Predicting the Confidence Maps of Shadow Detection Methods
TLDR
To measure the shadow detection confidence characteristics of an image, a novel relative confidence map prediction network (RCMPNet) is proposed and experimental results show that the proposed method outperforms multiple state-of-the-art shadow detection methods on four shadow detection benchmark datasets.
Learning-Based Shadow Detection in Aerial Imagery Using Automatic Training Supervision from 3D Point Clouds
Shadows, motion parallax, and occlusions pose significant challenges to vision tasks in wide area motion imagery (WAMI) including object identification and tracking. Although there are many…
Robust Shadow Detection by Exploring Effective Shadow Contexts
TLDR
This paper proposes a novel encoder-decoder style of shadow detection method where ECA acts as the main building block of the encoder to extract strong feature representations and the guidance to the classification process of the decoder.
Temporal Feature Warping for Video Shadow Detection
TLDR
This paper uses an optical flow based warping module to align and then combine features between frames to retrieve information from neighboring frames including both local details and high-level semantic information and shows results that outperforms the state-of-the-art video shadow detection method.
Annotation is easy: Learning to generate a shadow mask
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
TLDR
Experimental results show that the proposed Spatio-Temporal Interpolation Consistency Training (STICT) framework is better than most state of the art supervised, semi-supervised or unsupervised image/video shadow detection methods and other methods in related tasks.
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