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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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Kunlong Hong, Hongguang Wang and Bingbing Yuan
For the surface defects inspection task, operators need to check the defect in local detail images by specifying the location, which only the global 3D model reconstruction can?t satisfy. We explore how to address multi-type (original image, semantic ima...
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Huiyan Wu and Jun Huang
The main purpose of the joint entity and relation extraction is to extract entities from unstructured texts and extract the relation between labeled entities at the same time. At present, most existing joint entity and relation extraction networks ignore...
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Sunveg Nalwar, Kunal Shah, Ranjeet Vasant Bidwe, Bhushan Zope, Deepak Mane, Veena Jadhav and Kailash Shaw
Clouds play a vital role in Earth?s water cycle and the energy balance of the climate system; understanding them and their composition is crucial in comprehending the Earth?atmosphere system. The dataset ?Understanding Clouds from Satellite Images? conta...
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Jaehui Park
Semantic role labeling is an effective approach to understand underlying meanings associated with word relationships in natural language sentences. Recent studies using deep neural networks, specifically, recurrent neural networks, have significantly imp...
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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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Gang Zhang, Tao Lei, Yi Cui and Ping Jiang
Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challeng...
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