TY - GEN
T1 - CamoFocus
T2 - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
AU - Khan, Abbas
AU - Khan, Mustaqeem
AU - Gueaieb, Wail
AU - Saddik, Abdulmotaleb El
AU - De Masi, Giulia
AU - Karray, Fakhri
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/3
Y1 - 2024/1/3
N2 - Camouflage Object Detection (COD) involves the challenge of isolating a target object from a visually similar background, presenting a formidable challenge for learning algorithms. Drawing inspiration from state-of-the-art (SOTA) Focal Modulation Networks, our objective is to proficiently modulate the foreground and background components, thereby capturing the distinct features of each. We introduce a Feature Split and Modulation (FSM) module to attain this goal. This module efficiently separates the object from the background by utilizing foreground and background modulators guided by a supervisory mask. For enhanced feature refinement, we propose a Context Refinement Module (CRM), which considers features acquired from FSM across various spatial scales, leading to comprehensive enrichment and highly accurate prediction maps. Through extensive experimentation, we showcase the superiority of CamoFocus over recent SOTA COD methods. Our evaluations encompass diverse benchmark datasets, including CAMO, COD10K, CHAMELEON, and NC4K. The findings underscore the potential and significance of the proposed CamoFocus model and establish its efficacy in addressing the critical challenges of camouflage object detection.
AB - Camouflage Object Detection (COD) involves the challenge of isolating a target object from a visually similar background, presenting a formidable challenge for learning algorithms. Drawing inspiration from state-of-the-art (SOTA) Focal Modulation Networks, our objective is to proficiently modulate the foreground and background components, thereby capturing the distinct features of each. We introduce a Feature Split and Modulation (FSM) module to attain this goal. This module efficiently separates the object from the background by utilizing foreground and background modulators guided by a supervisory mask. For enhanced feature refinement, we propose a Context Refinement Module (CRM), which considers features acquired from FSM across various spatial scales, leading to comprehensive enrichment and highly accurate prediction maps. Through extensive experimentation, we showcase the superiority of CamoFocus over recent SOTA COD methods. Our evaluations encompass diverse benchmark datasets, including CAMO, COD10K, CHAMELEON, and NC4K. The findings underscore the potential and significance of the proposed CamoFocus model and establish its efficacy in addressing the critical challenges of camouflage object detection.
KW - Algorithms
KW - Algorithms
KW - and algorithms
KW - Applications
KW - Biomedical / healthcare / medicine
KW - formulations
KW - Image recognition and understanding
KW - Machine learning architectures
UR - http://www.scopus.com/inward/record.url?scp=85188805029&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85188805029&partnerID=8YFLogxK
U2 - 10.1109/WACV57701.2024.00146
DO - 10.1109/WACV57701.2024.00146
M3 - Conference contribution
AN - SCOPUS:85188805029
T3 - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
SP - 1423
EP - 1432
BT - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 January 2024 through 8 January 2024
ER -