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Fine-Grained Image Classification Network Based on Reinforcement and Complementary Learning
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DOI：10.1109/ACCESS.2024.3368379.png)
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Abstract
En 中文
There are subtle differences between single regions of the same subcategory in fine-grained images. At present, many fine-grained image classification networks often focus on a single region to determine the target category. However, in many cases, most discriminative features in fine-grained images are distributed in multiple local regions of the image, and it is not often enough for fine-grained image to rely solely on one region.To solve these problems, a new method is proposed. This method generates discriminative features through reinforcement learning and obtains complementary regions through complementary network. The reinforcement network and the complementary network learn through adversarial learning and improve the accuracy of fine-grained images classification.The method is tested on CUB200-2011,fine-grained Visual Classification of Aircraft, and Stanford dogs datasets and the results show adequate performance.
Keywords:
Feature extraction
Image classification
Convolutional neural networks
Training
Semantics
Reinforcement learning
Neural networks
Grain boundaries
Data models
Fine-grained image classification
inception-V3
complementary reinforcement learning
complementary learning
inter-class gap
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W