Camouflaged Object Segmentation
18 papers with code • 6 benchmarks • 7 datasets
Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by T.-N. Le et al. (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on CAMO dataset, COD dataset, and online demo.
( Image source: Anabranch Network for Camouflaged Object Segmentation )
Benchmarks
These leaderboards are used to track progress in Camouflaged Object Segmentation
COD10K
CAMO
NC4K
MoCA-Mask
CAMO++
R2C7K
Latest papers with no code
Open-Vocabulary Camouflaged Object Segmentation
With the proposed dataset and baseline, we hope that this new task with more practical value can further expand the research on open-vocabulary dense prediction tasks.
Diffusion Model for Camouflaged Object Detection
Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks.
The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation
In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation.
Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite
We also provide a benchmark suite for the task of camouflaged instance segmentation.
MirrorNet: Bio-Inspired Camouflaged Object Segmentation
Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively.
