YUV20K is a complexity-driven VCOD benchmark with 24k annotated frames, paired with a model using Motion Feature Stabilization via semantic primitives and Trajectory-Aware Alignment via deformable sampling that outperforms prior methods.
A survey of camouflaged object detection and beyond
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
RIDE applies Retinex-based homogeneous decomposition to improve foreground-background discriminability in concealed object segmentation tasks across multiple domains.
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
CATP prunes low-confidence tokens in COD Transformers and uses dual-path compensation to cut computation while preserving segmentation accuracy on boundary regions.
citing papers explorer
-
YUV20K: A Complexity-Driven Benchmark and Trajectory-Aware Alignment Model for Video Camouflaged Object Detection
YUV20K is a complexity-driven VCOD benchmark with 24k annotated frames, paired with a model using Motion Feature Stabilization via semantic primitives and Trajectory-Aware Alignment via deformable sampling that outperforms prior methods.
-
RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects
RIDE applies Retinex-based homogeneous decomposition to improve foreground-background discriminability in concealed object segmentation tasks across multiple domains.
-
Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
-
CATP: Confidence-Aware Token Pruning for Camouflaged Object Detection
CATP prunes low-confidence tokens in COD Transformers and uses dual-path compensation to cut computation while preserving segmentation accuracy on boundary regions.