Shadow Detection
37 papers with code • 1 benchmarks • 3 datasets
Latest papers
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery
Precise and efficient cloud and cloud shadow masking methods are required for the automated use of this data.
NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotation for shadow detection, joint learning of shadow detection and removal, and the introduction of new data augmentation technique "CutShadow" for shadow removal.
Delving into Dark Regions for Robust Shadow Detection
Our key insight to this problem is that existing methods typically learn discriminative shadow features from the whole image globally, covering the full range of intensity values, and may not learn the subtle differences between shadow and non-shadow pixels in dark regions.
AdapterShadow: Adapting Segment Anything Model for Shadow Detection
To adapt SAM for shadow images, trainable adapters are inserted into the frozen image encoder of SAM, since the training of the full SAM model is both time and memory consuming.
SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels
Existing shadow detection datasets often contain missing or mislabeled shadows, which can hinder the performance of deep learning models trained directly on such data.
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on complex backgrounds.
SAM-helps-Shadow:When Segment Anything Model meet shadow removal
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.
Explicit Visual Prompting for Universal Foreground Segmentations
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).
Detect Any Shadow: Segment Anything for Video Shadow Detection
Segment anything model (SAM) has achieved great success in the field of natural image segmentation.
When SAM Meets Shadow Detection
As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance.