{"type":"ScholarlyArticle","title":"Locate, Refine and Restore: A Progressive Enhancement Network for Camouflaged Object Detection","authors":[{"name":"Xiaofei Li","slug":"xiaofei-li","given":"Xiaofei","family":"Li","affiliation":"Laboratory for Big Data and Decision , National University of Defense Technology , China"},{"name":"Jiaxin Yang","slug":"jiaxin-yang","given":"Jiaxin","family":"Yang","affiliation":"Laboratory for Big Data and Decision , National University of Defense Technology , China"},{"name":"Shuohao Li","slug":"shuohao-li","given":"Shuohao","family":"Li","affiliation":"Laboratory for Big Data and Decision , National University of Defense Technology , China"},{"name":"Jun Lei","slug":"jun-lei","given":"Jun","family":"Lei","affiliation":"Laboratory for Big Data and Decision , National University of Defense Technology , China"},{"name":"Jun Zhang","slug":"jun-zhang","given":"Jun","family":"Zhang","affiliation":"Laboratory for Big Data and Decision , National University of Defense Technology , China"},{"name":"Dong Chen","slug":"dong-chen","given":"Dong","family":"Chen","affiliation":"Science and Technology on Information Systems Engineering Laboratory , National University of Defense Technology , China"}],"abstract":"Camouflaged Object Detection (COD) aims to segment objects that blend in with their surroundings. Most existing methods mainly tackle this issue by a single-stage framework, which tends to degrade performance in the face of small objects, low-contrast objects and objects with diverse appearances. In this paper, we propose a novel Progressive Enhancement Network (PENet) for COD by imitating the human visual detection system, which follows a three-stage detection process: locate objects, refine textures and restore boundary. Specifically, our PENet contains three key modules, i.e., the object location module (OLM), the group attention module (GAM) and the context feature restoration module (CFRM). The OLM is designed to position the object globally, the GAM is developed to refine both high-level semantic and low-level texture feature representation, and the CFRM is leveraged to effectively aggregate multi-level features for progressively restoring the clear boundary. Extensive results demonstrate that our PENet significantly outperforms 32 state-of-the-art methods on four widely used benchmark datasets","doi":"10.24963/ijcai.2023/124","url":"https://ijcai.org/proceedings/2023/124","pdf":"https://ijcai.org/proceedings/2023/0124.pdf","markdown":"https://ijcai.org/proceedings/2023/124.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/124","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":124,"pages":{"first":1116,"last":1124},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Bazzani et al., 2016] Loris Bazzani, Alessandra Bergamo, Dragomir Anguelov, and Lorenzo Torresani. 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