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Seung-Taek Kim and Hyo Jong Lee
The proposed lightweight hourglass network can be applied as an alternative to existing methods that use the hourglass model as a backbone network.
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Defeng He and Quande Wang
Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cro...
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Shiyu Zhang, Jianguo Kong, Chao Chen, Yabin Li and Haijun Liang
The rise of end-to-end (E2E) speech recognition technology in recent years has overturned the design pattern of cascading multiple subtasks in classical speech recognition and achieved direct mapping of speech input signals to text labels. In this study,...
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Zhongyu Sun, Wangping Zhou, Chen Ding and Min Xia
Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorith...
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Lizhen Jia, Yanyan Xu and Dengfeng Ke
Recent speech enhancement studies have mostly focused on completely separating noise from human voices. Due to the lack of specific structures for harmonic fitting in previous studies and the limitations of the traditional convolutional receptive field, ...
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Yundong Li, Xiaokun Wei and Hanlu Fan
Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision, plays important roles in downstream applications such as virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based methods g...
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Liang Han, Feng Liu and Kaifeng Chen
Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network wi...
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Kerang Cao, Jingyu Gao, Kwang-nam Choi and Lini Duan
To classify the image material on the internet, the deep learning methodology, especially deep neural network, is the most optimal and costliest method of all computer vision methods. Convolutional neural networks (CNNs) learn a comprehensive feature rep...
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Hongyan Xu, Xiu Su, Yi Wang, Huaiyu Cai, Kerang Cui and Xiaodong Chen
Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model ba...
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Hayat Ullah and Arslan Munir
The recognition of human activities using vision-based techniques has become a crucial research field in video analytics. Over the last decade, there have been numerous advancements in deep learning algorithms aimed at accurately detecting complex human ...
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Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a...
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Hao Wang and Nanfeng Xiao
In order to better utilize and protect marine organisms, reliable underwater object detection methods need to be developed. Due to various influencing factors from complex and changeable underwater environments, the underwater object detection is full of...
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In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-superv...
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Quanchun Jiang, Olamide Timothy Tawose, Songwen Pei, Xiaodong Chen, Linhua Jiang, Jiayao Wang and Dongfang Zhao
In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-superv...
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Wenji Yang and Xiaoying Qiu
The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model na...
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