Multi-perspective decoupling enhancement and integration for fine-grained classification
To address the significant intra-class variation caused by external factors such as background environment,lighting conditions,sample posture,and shooting angle in fine-grained image classification,this paper proposes a fine-grained classification algorithm based on multi-perspective decoupling enhancement integration.Firstly,to re-duce the interference of external factors in images,a multi-perspective attention(MPA)module is designed.This module decomposes the model into several perspectives,forcing each perspective to focus on different scales,thus decoupling the interference factors.By modeling features with self-attention,each perspective is guided to further mine key features.Secondly,a progressive dynamic weighted fusion(PDWF)strategy is proposed to effectively in-tegrate the decoupled multi-perspective information.This strategy dynamically adjusts the fusion coefficient by ac-quiring channel and spatial relationships from different perspectives,achieving high-order fusion of multi-scale in-formation.Lastly,a progressive training method is adopted to facilitate perspective interaction,further capturing and integrating complementary semantic information from multi-scale features.Experiments are conducted on three public datasets,CUB-200-2011,Stanford-Cars,and FGVC-Aircraft,and the results show that the proposed method achieves classification accuracy rates of 90.5％,95.5％,and 94.2％,respectively,which outperforms current mainstream methods for fine-grained image classification tasks.
万方数据
维普
