Shadow Detection
32 papers with code • 1 benchmarks • 3 datasets
SBU
SOBA
Most implemented papers
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
IsHYuhi/ST-CGAN_Stacked_Conditional_Generative_Adversarial_Networks
•
•
CVPR 2018
Specifically, a shadow image is fed into the first generator which produces a shadow detection mask.
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation
Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis.
Instance Shadow Detection
stevewongv/InstanceShadowDetection
•
•
CVPR 2020
Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results.
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
xw-hu/FSDNet
•
•
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world.
SpA-Former: Transformer image shadow detection and removal via spatial attention
zhangbaijin/spa-former-shadow-removal
•
•
In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image.
Instance Shadow Detection with A Single-Stage Detector
stevewongv/InstanceShadowDetection
•
•
This paper formulates a new problem, instance shadow detection, which aims to detect shadow instance and the associated object instance that cast each shadow in the input image.
Explicit Visual Prompting for Universal Foreground Segmentations
nifangbaage/explicit-visual-prompt
•
•
We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP).
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
In recent years, various shadow detection methods from a single image have been proposed and used in vision systems; however, most of them are not appropriate for the robotic applications due to the expensive time complexity.
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation
lmhieu612/adnet_demo
•
•
ECCV 2018
The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net's shadow predictions.
Direction-aware Spatial Context Features for Shadow Detection
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.
