.png){kind=icon}
返回
Pixel-Centric Context Perception Network for Camouflaged Object Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3319323.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Camouflaged object detection (COD) aims to identify object pixels visually embedded in the background environment. Existing deep learning methods fail to utilize the context information around different pixels adequately and efficiently. In order to solve this problem, a novel pixel-centric context perception network (PCPNet) is proposed, the core of which is to customize the personalized context of each pixel based on the automatic estimation of its surroundings. Specifically, PCPNet first employs an elegant encoder equipped with the designed vital component generation (VCG) module to obtain a set of compact features rich in low-level spatial and high-level semantic information across multiple subspaces. Then, we present a parameter-free pixel importance estimation (PIE) function based on multiwindow information fusion. Object pixels with complex backgrounds will be assigned with higher PIE values. Subsequently, PIE is utilized to regularize the optimization loss. In this way, the network can pay more attention to those pixels with higher PIE values in the decoding stage. Finally, a local continuity refinement module (LCRM) is used to refine the detection results. Extensive experiments on four COD benchmarks, five salient object detection (SOD) benchmarks, and five polyp segmentation benchmarks demonstrate the superiority of PCPNet with respect to other state-of-the-art methods.
Keyword:
Camouflaged object detection (COD)
salient object detection (SOD)
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
LID, LEED, and Alternative Rating Systems—Integrating Low Impact Development Techniques with Green Building DesignLID，LEED和替代评级系统-将低影响开发技术与绿色建筑设计相结合
PREAI
分享收藏Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians在结肠镜检查中准确突出显示息肉的wm-dova地图: 医生的验证与显著性地图
PREAI
分享收藏Satellite‐derived NDVI underestimates the advancement of alpine vegetation growth over the past three decades.png)
Ecology
OAAI
EcologyIF0
OAAI分享收藏
查看更多内容