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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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Fei Yan, Hui Zhang, Yaogen Li, Yongjia Yang and Yinping Liu
Raw image classification datasets generally maintain a long-tailed distribution in the real world. Standard classification algorithms face a substantial issue because many labels only relate to a few categories. The model learning processes will tend tow...
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Priyank Kalgaonkar and Mohamed El-Sharkawy
Object detection, a more advanced application of computer vision than image classification, utilizes deep neural networks to predict objects in an input image and determine their locations through bounding boxes. The field of artificial intelligence has ...
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Hong Xu, Haozun Sun, Lubin Wang, Xincan Yu and Tianyue Li
The visual quality and spatial distribution of architectural styles represent a city?s image, influence inhabitants? living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional ...
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János Hollósi, Áron Ballagi and Claudiu Radu Pozna
Classifying digital images using neural networks is one of the most fundamental tasks within the field of artificial intelligence. For a long time, convolutional neural networks have proven to be the most efficient solution for processing visual data, su...
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Wentao Lv, Fan Li, Shijie Luo and Jie Xiang
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that can reduce quality of life and burden families. However, there is a lack of objectivity in clinical diagnosis, so it is very important to develop a method for early and accurate...
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Yi-Quan Li, Hao-Sen Chang and Daw-Tung Lin
In the field of computer vision, large-scale image classification tasks are both important and highly challenging. With the ongoing advances in deep learning and optical character recognition (OCR) technologies, neural networks designed to perform large-...
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Giorgia Franchini, Micaela Verucchi, Ambra Catozzi, Federica Porta and Marco Prato
It is well known that biomedical imaging analysis plays a crucial role in the healthcare sector and produces a huge quantity of data. These data can be exploited to study diseases and their evolution in a deeper way or to predict their onsets. In particu...
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Zhonglin Ji, Yu Zhu, Yaozhong Pan, Xiufang Zhu and Xuechang Zheng
Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly i...
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Pi-Yun Chen, Xuan-Hao Zhang, Jian-Xing Wu, Ching-Chou Pai, Jin-Chyr Hsu, Chia-Hung Lin and Neng-Sheng Pai
Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic bre...
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