Camouflaged Object Segmentation
17 papers with code • 6 benchmarks • 8 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 )
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.
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
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.
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.
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).
In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection.