ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
Source abstract
Abstract
Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, this paper proposes two attention embedding methods based on ConvNeXt network and designs a new bilinear CBAM; simultaneously, a multiscale, multi-perspective and all-around attention framework is proposed, which is then applied in ConvNeXt. Experimental verification shows that the accuracy rate of the improved ConvNeXt for fine-grained image classification reaches 87.8%, 91.2%, and 93.2% on fine-grained classification datasets CUB-200-2011, Stanford Cars, and FGVC Aircraft, respectively, showing increases of 2.7%, 0.3% and 0.4%, respectively, compared to those of the original network without optimization, and increases of 3.7%, 8.0% and 2.0%, respectively, compared to those of the traditional BCNN. In addition, ablation experiments are set up to verify the effectiveness of the proposed attention framework.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning · Machine Learning and Data Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guangxi Liugong Machinery Co., Ltd., Liuzhou 545006, China
Guangxi Earthmoving Machinery Collaborative Innovation Center, Guangxi Science and Technology University, Liuzhou 545006, China
College of Mechanical and Automotive Engineering, Guangxi Science and Technology University, Liuzhou 545006, China
Funding
Guangxi Key Technologies R&D Program · Guangxi Science and Technology Major Project · Guangxi Science and Technology Project
References (33)
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