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Ensemble relation network with multi-level measure

The Journal of China Universities of Posts and Telecommunications 2022年第3期29卷 15-24,33页

作者： Li Xiaoxu Qu Xue Cao Jie College of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Engineering Research Center of Urban Railway Transportation of Gansu Province Lanzhou 730050China

fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shot learning image classification methods only use top-level features and adopt a single measure. In that way, the local features of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure(ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space to compare the similarity between the samples in the local features, and finally integrates the similarity scores from the feature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details and global information of different grains. Experimental results on different fine-grained datasets show that the proposed method achieves good classification performance and also proves its rationality.

关键词： fine-grained image classification few-shot learning local feature learning metric learning

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A Survey on Deep Learning-based fine-grained Object classification and Semantic Segmentation

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International Journal of Automation and computing 2017年第2期14卷 119-135页

作者： Bo Zhao Jiashi Feng Xiao Wu Shuicheng Yan School of Information Science and Technology Southwest Jiaotong University Department of Electrical and Computer Engineering National University of Singapore

The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks （CNNs）, part detection based, ensemble of networks based and visual attention based fine-grained image classification approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively.

关键词： Deep learning fine-grained image classification semantic segmentation convolutional neural network (CNN) recurrentneural network (RNN)

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