Year: 2024
Type: conference-paper
Abstract: Due to significant intra-class variations and subtle inter-class differences, fine-grained images often pose challenges.In this paper, we propose a multi-level attention-enhanced network framework to address this issue.First, we employ convolutional features to crop the original images to focus on the main part and mitigate the interference of backgrounds on classification.Second, we design a scale-adaptive local discriminative region generation module based on the main part's images, effectively selecting locally detailed and rich image patches.It is worth noting that our proposed method only utilizes image-level labels.Finally, we extensively tested our method on three benchmark datasets, including the CUB-200-2011, Stanford Cars, and FGVC Aircraft.The experimental results demonstrate that our method achieves comparable or better classification performance than previous state-of-the-art fine-grained visual classification methods.
Authors: Kaifeng Ding, Caolin Yang, Chengzhuan Yang, Zhonglong Zheng
Institution: Zhejiang Normal University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G5775775103, G8109244785, G8913174516
OpenAlex ID: W4407601433