Integration of Multi-scale CAM and Attention for Weakly Supervised Defects Localization on Surface Defective Apple

• 영문명: Integration of Multi-scale CAM and Attention for Weakly Supervised Defects Localization on Surface Defective Apple
• 발행기관: 한국스마트미디어학회
• 저자명: Nguyen Bui Ngoc Han Ju Hwan Lee Gyeong Ju Kwon Jin Young Kim
• 간행물 정보: 『스마트미디어저널』Vol12, No.9, 45~59쪽, 전체 15쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2023.10.30

영문 초록

Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated to produce multi-head attention maps as an auxiliary detector. By integrating the CNN-based CAMs and attention maps, our approach localizes defective regions without requiring bounding box or pixel-level supervision during training. We evaluate our approach on a dataset of apple images with only image-level labels of defect categories. Experiments demonstrate our proposed method aligns with several Object Detection models performance, hold a promise for improving localization.

목차

Ⅰ. 서론

Ⅰ. INTRODUCTION

Ⅱ. RELATED WORK

Ⅲ. PROPOSED METHOD

Ⅳ. EXPERIMENTS

Ⅴ. Discussion

Ⅵ. Conclusion

REFERENCES

키워드

WSOL multi-scale CAM attention Surface defective apple