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
Most implemented papers
EGNet: Edge Guidance Network for Salient Object Detection
mindspore-ai/models
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ICCV 2019
In the second step, we integrate the local edge information and global location information to obtain the salient edge features.
Boundary-Aware Segmentation Network for Mobile and Web Applications
NathanUA/BASNet
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In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
BASNet: Boundary-Aware Salient Object Detection
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Camouflaged Object Detection
DengPingFan/SINet
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CVPR 2020
We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings.
Generative Transformer for Accurate and Reliable Salient Object Detection
For the former, we apply transformer to a deterministic model, and explain that the effective structure modeling and global context modeling abilities lead to its superior performance compared with the CNN based frameworks.
Anabranch Network for Camouflaged Object Segmentation
Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy.
Explicit Visual Prompting for Universal Foreground Segmentations
nifangbaage/explicit-visual-prompt
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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).
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
wuzhe71/CPD
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CVPR 2019
In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection.
Concealed Object Detection
We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background.
