ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model

ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model

Blog Article

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 Nasal Spray 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, Wooden Bottle Opener Keychain 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.