Shadow Detection

21 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

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 16 Nov 2019

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/spatial-transformer-shadow-removal 22 Jun 2022

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 11 Jul 2022

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.

Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network

sepidehhosseinzadeh/Fast-Shadow-Detection 26 Sep 2017

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.

Direction-aware Spatial Context Features for Shadow Detection

xw-hu/DSC CVPR 2018

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.

Direction-aware Spatial Context Features for Shadow Detection and Removal

xw-hu/DSC 12 May 2018

This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.

Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection

zijundeng/BDRAR ECCV 2018

Second, we develop a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers by deploying two series of RAR modules in the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another series from shallow to deep layers.