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A survey on deep learning-based fine-grained object classification and semantic segmentation
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A survey on deep learning-based fine-grained object classification and semantic segmentation
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A survey on deep learning-based fine-grained object classification and semantic segmentation
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Journal Article
A survey on deep learning-based fine-grained object classification and semantic segmentation
2017
Overview
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.
Publisher
Springer Nature B.V
