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Naoko Nitta, Kazuaki Nakamura and Noboru Babaguchi
While visual appearances play a main role in recognizing the concepts captured in images, additional information can provide complementary information for fine-grained image recognition, where concepts with similar visual appearances such as species of b...
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Yejin Lee, Suho Lee and Sangheum Hwang
Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass dif...
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Bhishan Bhandari, Geonu Lee and Jungchan Cho
Action recognition is an application that, ideally, requires real-time results. We focus on single-image-based action recognition instead of video-based because of improved speed and lower cost of computation. However, a single image contains limited inf...
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Xiaojuan Wang and Weilan Wang
As there is a lack of public mark samples of Tibetan historical document image characters at present, this paper proposes an unsupervised Tibetan historical document character recognition method based on deep learning (UD-CNN). Firstly, using the Tibetan...
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Xin Jin, Cheng Lin, Jiangtao Ji, Wenhao Li, Bo Zhang and Hongbin Suo
The extraction of navigation lines plays a crucial role in the autonomous navigation of agricultural robots. This work offers a method of ridge navigation route extraction, based on deep learning, to address the issues of poor real-time performance and l...
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Kai Ma, Ming-Jun Nie, Sen Lin, Jianlei Kong, Cheng-Cai Yang and Jinhao Liu
Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the diff...
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Yadong Yang, Xiaofeng Wang, Quan Zhao and Tingting Sui
The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional network...
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