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Auto-painter: Cartoon image generation from sketch by using conditional generative adversarial networks

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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. learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose the auto-painter model which can automatically generate compatible colors for a sketch. The new model is not only capable of painting hand-draw sketch with proper colors, but also allowing users to indicate preferred colors. Experimental results on two sketch datasets show that the auto-painter performs better that existing image-to-image methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Generative adversarial networks

A sequential guiding network with attention for image captioning

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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 automatic description generation from natural images. In this challenge, the encoder-decoder framework has achieved promising performance when a convolutional neural network (CNN) is used as image encoder and a recurrent neural network (RNN) as decoder. In this paper, we introduce a sequential guiding network that guides the decoder during word generation. The new model is an extension of the encoder-decoder framework with attention that has an additional guiding long short-term memory (LSTM) and can be trained in an end-to-end manner by using image/descriptions pairs. We validate our approach by conducting extensive experiments on a benchmark dataset, i.e., MS COCO Captions. The proposed model achieves significant improvement comparing to the other state-of-the-art deep learning models. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

A light rule-based approach to english subject-verb agreement errors on the third person singular forms 29

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A light rule-based approach to english subject-verb agreemen...

29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Wang, Yuzhu Zhao, Hai Shi, Dan Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China LangYing NLP Research Institute Shanghai Lang Ying Education Technology Co. Ltd China

Verb errors are one of the most common grammar errors made by non-native writers of English. This work especially focus on an important type of verb usage errors, subject-verb agreement for the third person singular forms, which has a high proportion in errors made by non-native English learners. Existing work has not given a satisfied solution for this task, in which those using supervised learning method usually fail to output good enough performance, and rule-based methods depend on advanced linguistic resources such as syntactic parsers. In this paper, we propose a rule-based method to detect and correct the concerned errors. The proposed method relies on a series of rules to automatically locate subject and predicate in four types of sentences. The evaluation shows that the proposed method gives state-of-The-Art performance with quite limited linguistic resources.

关键词： Errors

Simulation and HRI recent perspectives with the MORSE simulator

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8810卷 13-24页

作者： Lemaignan, Séverin Hanheide, Marc Karg, Michael Khambhaita, Harmish Kunze, Lars Lier, Florian Lütkebohle, Ingo Milliez, Grégoire CHILI Lab. EPFL Lausanne Switzerland Centre for Autonomous Systems University of Lincoln United Kingdom IAS Technische Universität München Germany LAAS/CNRS Université de Toulouse France Intelligent Robotics Lab. University of Birmingham United Kingdom CITEC Bielefeld University Germany Machine Learning and Robotics Lab. Universität Stuttgart Germany

Simulation in robotics is often a love-hate relationship: while simulators do save us a lot of time and effort compared to regular deployment of complex software architectures on complex hardware, simulators are also known to evade many of the real issues that robots need to manage when they enter the real world. Because humans are the paragon of dynamic, unpredictable, complex, real world entities, simulation of human-robot interactions may look condemn to fail, or, in the best case, to be mostly useless. This collective article reports on five independent applications of the MORSE simulator in the field of human-robot interaction: It appears that simulation is already useful, if not essential, to successfully carry out research in the field of HRI, and sometimes in scenarios we do not anticipate. © 2014 Springer International Publishing Switzerland.

关键词： Simulators

Motif iteration model for network representation

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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 the structure of a social network is a key in social media mining. In this paper, we propose the Motif Iteration Model (MIM) to represent the structure of a social network. As the name suggested, the new model is based on iteration of basic network motifs. In order to better show the properties of the model, a heuristic and greedy algorithm called Vertex Reordering and Arranging (VRA) is proposed by studying the adjacency matrix of the three-vertex undirected network motifs. The algorithm is for mapping from the adjacency matrix of a network to a binary image, it shows a new perspective of network structure visualization. In summary, this model provides a useful approach towards building link between images and networks and offers a new way of representing the structure of a social network. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

DualVD: An adaptive dual encoding model for deep visual understanding in visual dialogue

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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 any objects, relationships or semantics. The key challenge in Visual Dialogue task is thus to learn a more comprehensive and semantic-rich image representation which may have adaptive attentions on the image for variant questions. In this research, we propose a novel model to depict an image from both visual and semantic perspectives. Specifically, the visual view helps capture the appearance-level information, including objects and their relationships, while the semantic view enables the agent to understand high-level visual semantics from the whole image to the local regions. Futhermore, on top of such multiview image features, we propose a feature selection framework which is able to adaptively capture question-relevant information hierarchically in fine-grained level. The proposed method achieved state-of-the-art results on benchmark Visual Dialogue datasets. More importantly, we can tell which modality (visual or semantic) has more contribution in answering the current question by visualizing the gate values. It gives us insights in understanding of human cognition in Visual Dialogue. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

CogTree: Cognition tree loss for unbiased scene graph generation

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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 scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is availab.e at: https://***/CYVincent/Scene-Graph-Transformer-CogTree. Copyright © 2020, The Authors. All rights reserved.

关键词： Forestry

Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval

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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 distance between concepts in different modalities can be well modeled. For crossmodal information retrieval between images and texts, existing work mostly uses off-the-shelf Convolutional Neural Network (CNN) for image feature extraction. For texts, word-level features such as bag-of-words or word2vec are employed to build deep learning models to represent texts. Besides word-level semantics, the semantic relations between words are also informative but less explored. In this paper, we model texts by graphs using similarity measure based on word2vec. A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval. One path utilizes Graph Convolutional Network (GCN) for text modeling based on graph representations. The other path uses a neural network with layers of nonlinearities for image modeling based on off-the-shelf features. The model is trained by a pairwise similarity loss function to maximize the similarity of relevant text-image pairs and minimize the similarity of irrelevant pairs. Experimental results show that the proposed model outperforms the state-of-the-art methods significantly, with 17% improvement on accuracy for the best case. Copyright © 2018, The Authors. All rights reserved.

关键词： Information retrieval

Stock volatility prediction using recurrent neural networks with sentiment analysis

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arXiv 2017年

作者： Liu, Yifan Qin, Zengchang Li, Pengyu Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 School of Mechanical Engineering and Automation Beihang University Beijing100191 School of Biological Science and Medical Engineering Beihang University Beijing100191 China

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatil-ity in the Chinese market. We have lab.led the sentiment of the online financial posts and make the dataset public availab.e for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting