TY - GEN
T1 - ACNet
T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
AU - Min, Kyungseo
AU - Lee, Gun Hee
AU - Lee, Seong Whan
N1 - Funding Information:
*This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Government of South Korea (No. 2017-0-00451, Development of BCI-based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University). We thank Samsung Research for generously supporting the project.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Computer-aided diagnosis has recently received attention for its advantage of low cost and time efficiency. Although deep learning played a major role in the recent success of acne detection, there are still several challenges such as color shift by inconsistent illumination, variation in scales, and high density distribution. To address these problems, we propose an acne detection network which consists of three components, specifically: Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention. First, Composite Feature Refinement integrates semantic information and fine details to enrich feature representation, which mitigates the adverse impact of imbalanced illumination. Then, Dynamic Context Enhancement controls different receptive fields of multi-scale features for context enhancement to handle scale variation. Finally, Mask-Aware Multi-Attention detects densely arranged and small acne by suppressing uninformative regions and highlighting probable acne regions. Experiments are performed on acne image dataset ACNE04 and natural image dataset PASCAL VOC 2007. We demonstrate how our method achieves the state-of-the-art result on ACNE04 and competitive performance with previous state-of-the-art methods on the PASCAL VOC 2007.
AB - Computer-aided diagnosis has recently received attention for its advantage of low cost and time efficiency. Although deep learning played a major role in the recent success of acne detection, there are still several challenges such as color shift by inconsistent illumination, variation in scales, and high density distribution. To address these problems, we propose an acne detection network which consists of three components, specifically: Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention. First, Composite Feature Refinement integrates semantic information and fine details to enrich feature representation, which mitigates the adverse impact of imbalanced illumination. Then, Dynamic Context Enhancement controls different receptive fields of multi-scale features for context enhancement to handle scale variation. Finally, Mask-Aware Multi-Attention detects densely arranged and small acne by suppressing uninformative regions and highlighting probable acne regions. Experiments are performed on acne image dataset ACNE04 and natural image dataset PASCAL VOC 2007. We demonstrate how our method achieves the state-of-the-art result on ACNE04 and competitive performance with previous state-of-the-art methods on the PASCAL VOC 2007.
KW - Acne detection
KW - Computer-aided diagnosis
KW - Object detection
UR - http://www.scopus.com/inward/record.url?scp=85124303295&partnerID=8YFLogxK
U2 - 10.1109/SMC52423.2021.9659243
DO - 10.1109/SMC52423.2021.9659243
M3 - Conference contribution
AN - SCOPUS:85124303295
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2724
EP - 2729
BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 October 2021 through 20 October 2021
ER -