Advances in deep concealed scene understanding
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …
objects exhibiting camouflage. The current boom in terms of techniques and applications …
Camouflaged object detection with feature decomposition and edge reconstruction
Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …
camouflaged objects visually blended into the surrounding backgrounds. COD is a …
Rethinking camouflaged object detection: Models and datasets
H Bi, C Zhang, K Wang, J Tong… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Camouflaged object detection (COD) is an emerging visual detection task, which aims to
locate and distinguish the disguised target in complex backgrounds by imitating the human …
locate and distinguish the disguised target in complex backgrounds by imitating the human …
Zoom in and out: A mixed-scale triplet network for camouflaged object detection
Detecting camouflaged object in frequency domain
Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …
their environment, which has various downstream applications in fields such as medicine …
Concealed object detection
We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …
identify objects that are visually embedded in their background. The high intrinsic similarities …
Feature shrinkage pyramid for camouflaged object detection with transformers
Vision transformers have recently shown strong global context modeling capabilities in
camouflaged object detection. However, they suffer from two major limitations: less effective …
camouflaged object detection. However, they suffer from two major limitations: less effective …
Uncertainty-guided transformer reasoning for camouflaged object detection
Spotting objects that are visually adapted to their surroundings is challenging for both
humans and AI. Conventional generic/salient object detection techniques are suboptimal for …
humans and AI. Conventional generic/salient object detection techniques are suboptimal for …
Can sam segment anything? when sam meets camouflaged object detection
SAM is a segmentation model recently released by Meta AI Research and has been gaining
attention quickly due to its impressive performance in generic object segmentation …
attention quickly due to its impressive performance in generic object segmentation …