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检索条件"机构=Intelligent Computing & Machine Learning Lab"
75 条 记 录,以下是51-60 订阅
排序:
Logical parsing from natural language based on a neural translation model
arXiv
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arXiv 2017年
作者: Li, Liang Li, Pengyu Liu, Yifan Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China Biomedical Imaging and Informatics Lab School of Biological Science and Medical Engineering Beihang University Beijing100191 China
Semantic parsing has emerged as a significant and powerful paradigm for natural language interface and question answering systems. Traditional methods of building a semantic parser rely on high-quality lexicons, hand-... 详细信息
来源: 评论
Followmeup sports: new benchmark for 2D human keypoint recognition
arXiv
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arXiv 2019年
作者: Huang, Ying Sun, Bin Kan, Haipeng Zhuang, Jiankai Qin, Zengchang Alibaba Business School Hangzhou Normal University Hangzhou China Keep Inc. Beijing China Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China
Human pose estimation has made significant advancement in recent years. However, the existing datasets are limited in their coverage of pose variety. In this paper, we introduce a novel benchmark"FollowMeUp Sport... 详细信息
来源: 评论
Emotion classification with data augmentation using generative adversarial networks  22nd
Emotion classification with data augmentation using generati...
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22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
作者: Zhu, Xinyue Liu, Yifan Li, Jiahong Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China School of Electronic Engineering Bejing University of Posts and Telecommunications Beijing China Beijing San Kuai Yun Technology Co. Ltd. Beijing China School of Biological Science and Medical Engineering Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing China
It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an examp... 详细信息
来源: 评论
An Evolutionary Model for Efficient Transportation Networks
An Evolutionary Model for Efficient Transportation Networks
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International Conference on intelligent Human-machine Systems and Cybernetics, IHMSC
作者: Ian Huang Mei Chen William Yang Zengchang Qin International School of Beijing Baijing China Department of Electrical Engineering University of Southern California USA Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beihang University China
In this paper, we present a model to automatically generate efficient transportation networks given a simulated urban environment with predefined population distributions and other physical constraints. Based on the e... 详细信息
来源: 评论
Auto-painter: Cartoon image generation from sketch by using conditional generative adversarial networks
arXiv
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arXiv 2017年
作者: Liu, Yifan Qin, Zengchang Luo, Zhenbo Wang, Hua Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China Samsung RandD Institute China Beijing 18F TaiTangGong Plaza Beijing100028 China
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learning from a large collection of images.... 详细信息
来源: 评论
A sequential guiding network with attention for image captioning
arXiv
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arXiv 2018年
作者: Sow, Daouda Qin, Zengchang Niasse, Mouhamed Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China Keep Labs Keep Inc. Beijing China School of EEE North China Electric Power University China
The recent advances of deep learning in both computer vision (CV) and natural language processing (NLP) provide us a new way of understanding semantics, by which we can deal with more challenging tasks such as automat... 详细信息
来源: 评论
Motif iteration model for network representation
arXiv
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arXiv 2017年
作者: Lv, Lintao Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beihang University Beijing Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing Beijing100191 China
Social media mining has become one of the most popular research areas in Big Data with the explosion of social networking information from Facebook, Twitter, LinkedIn,Weibo and so on. Understanding and representing th... 详细信息
来源: 评论
DualVD: An adaptive dual encoding model for deep visual understanding in visual dialogue
arXiv
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arXiv 2019年
作者: Jiang, Xiaoze Yu, Jing Qin, Zengchang Zhuang, Yingying Zhang, Xingxing Hu, Yue Wu, Qi Institute of Information Engineering Chinese Academy of Sciences Beijing China Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China Microsoft Research Asia Beijing China University of Adelaide Australia
Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue involves multiple questions which cover a broad range of visual content that could be related to ... 详细信息
来源: 评论
CogTree: Cognition tree loss for unbiased scene graph generation
arXiv
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arXiv 2020年
作者: Yu, Jing Chai, Yuan Wang, Yujing Hu, Yue Wu, Qi Institute of Information Engineering Chinese Academy of Sciences Beijing China Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China Key Laboratory of Machine Perception MOE School of EECS Peking University Beijing China University of Adelaide Australia
Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world s... 详细信息
来源: 评论
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
arXiv
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arXiv 2018年
作者: Yu, Jing Lu, Yuhang Qin, Zengchang Liu, Yanbing Tan, Jianlong Guo, Li Zhang, Weifeng Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Intelligent Computing and Machine Learning Lab School of Asee Beihang University China Hangzhou Dianzi University China
Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a common semantic space, in which dist... 详细信息
来源: 评论