Fine-Grained Visual Categorization with 2D-Warping

Author(s):  
Harald Hanselmann ◽  
Hermann Ney
2017 ◽  
Vol 61 (1) ◽  
Author(s):  
Lihua Guo ◽  
Chenggang Guo ◽  
Lei Li ◽  
Qinghua Huang ◽  
Yanshan Li ◽  
...  

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.


Author(s):  
Xianjie Mo ◽  
Tingting Wei ◽  
Hengmin Zhang ◽  
Qiong Huang ◽  
Wei Luo

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76776-76786 ◽  
Author(s):  
Jingyun Liang ◽  
Jinlin Guo ◽  
Yanming Guo ◽  
Songyang Lao

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