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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hengtong Hu Lingxi Xie Richang Hong Qi Tian School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Huawei Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval. There are two main frameworks for CMH, differing from each other in whether semantic supervision is required. Compared to the unsupervised methods, the supervised methods often enjoy more accurate results, but require much heavier labors in data annotation. In this paper, we propose a novel approach that enables guiding a supervised method using outputs produced by an unsupervised method. Specifically, we make use of teacher-student optimization for propagating knowledge. Experiments are performed on two popular CMH benchmarks, i.e., the MIRFlickr and NUS-WIDE datasets. Our approach outperforms all existing unsupervised methods by a large margin.

关键词： Training Semantics Computational modeling Feature extraction Dogs Training data Task analysis

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Rankings never really quantify contributions: A quantitative and qualitative study on universities and their libraries

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Journal of Library Science in China 2020年 117-126页

作者： Shelia X.WEI Ronda J.ZHANG Howell Y.WANG CAO Cong Fred Y.YE International Joint Informatics Laboratory & Jiangsu Key Laboratory of Data Engineering and Knowledge Services School of Information Management Nanjing University Nottingham University Business School University of Nottingham

By comparing quantitative ranking with qualitative contributions, we reveal that academic assessment has to put real contributions ahead of quantitative indicators and that rankings have nothing to do with universities’ and their libraries’ true values. The greatness of a university lies in its impacts on the progress for human knowledge and the promotion for social development. Although ranking of universities by way of quantitative indicators can reflect some information, we should pay more attention to qualitative contributions.

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Recommending courses in MOOCs for jobs: An auto weak supervision approach

arXiv

引用

arXiv 2020年

作者： Hao, Bowen Zhang, Jing Li, Cuiping Chen, Hong Yin, Hongzhi Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education School of Information Renmin University of China China School of Information Technology and Electrical Engineering The University of Queensland Australia

The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework (AutoWeakS) via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

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Relation extraction with proactive domain adaptation strategy 11

Relation extraction with proactive domain adaptation strateg...

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11th IEEE International Conference on knowledge Graph, ICKG 2020

作者： Zhong, Lingfeng Zhu, Yi Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China Yangzhou University School of Computer Science and Technology Yangzhou China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Relation extraction is an important information extraction task in many Natural Language Processing (NLP) applications, such as automatic knowledge graph construction, question answering, sentiment analysis, etc. However, relation extraction suffers from inappropriate associations between entities when the background knowledge of corpus is insufficiency. Despite the preprocessed external word vector bases can ease this problem, how to find a single word vector base as domain knowledge that contains all the required knowledge features is a huge challenge, and relation extraction with background knowledge is still open to further optimization. To address this problem, in this paper, we propose Relation Extraction method with Proactive Domain Adaptation Strategy (REPDAS for short) to introduce more knowledge features from different knowledge bases. More specifically, firstly, a convolutional network with a parameter-sharing layer is introduced for relation extraction, and word seeds that are important to relational feature exploitation are proactively picked by an attention mechanism during training. Secondly, the proactively-chosen word seeds and the previous parameter-sharing layer are utilized to establish a map between different domains. Our proposed method selectively avails both background knowledge and contextual features for relation extraction by incorporating the convolutional neural network with the proactively domain adaptation strategy. Experiments show that our method effectively enhances the performance of relation extraction compared with other baselines. © 2020 IEEE.

关键词： Domain Adaptation knowledge Graph Relation Extraction

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Creating something from nothing: Unsupervised knowledge distillation for cross-modal hashing

arXiv

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arXiv 2020年

作者： Hu, Hengtong Xie, Lingxi Hong, Richang Tian, Qi School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Huawei Inc.

关键词： Supervised learning

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Multi-fuzzy-objective graph pattern matching with big graph data

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Journal of database Management 2019年第4期30卷 24-40页

作者： Li, Lei Zhang, Fang Liu, Guanfeng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Zhongxing Telecommunication Equipment Corporation Nanjing China Macquarie University Sydney Australia

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multifuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively. © 2019,IGI Global.

关键词： Genetic algorithms

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One-bit supervision for image classification 20

One-bit supervision for image classification

引用

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Hengtong Hu Lingxi Xie Zewei Du Richang Hong Qi Tian Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology and School of Computer Science and Information Engineering Hefei University of Technology Huawei Inc.

ISBN: (纸本)9781713829546

This paper presents one-bit supervision, a novel setting of learning from incomplete annotations, in the scenario of image classification. Instead of training a model upon the accurate label of each sample, our setting requires the model to query with a predicted label of each sample and learn from the answer whether the guess is correct. This provides one bit (yes or no) of information, and more importantly, annotating each sample becomes much easier than finding the accurate label from many candidate classes. There are two keys to training a model upon one-bit supervision: improving the guess accuracy and making use of incorrect guesses. For these purposes, we propose a multi-stage training paradigm which incorporates negative label suppression into an off-the-shelf semi-supervised learning algorithm. In three popular image classification benchmarks, our approach claims higher efficiency in utilizing the limited amount of annotations.

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On the Stability and Generalization of Triplet Learning

arXiv

引用

arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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An Open-Source Algorithm for Standardized Bullseye Visualization of High-Resolution Cardiac Ventricular data: UNISYS

An Open-Source Algorithm for Standardized Bullseye Visualiza...

引用

Computers in Cardiology (CinC)

作者： Job Stoks Uyen C Nguyen Ralf Peeters Paul GA Volders Matthijs JM Cluitmans BIOMED Hasselt University Hasselt Belgium Maastricht University Maastricht the Netherlands Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands

ISBN: (数字)9781728173825

ISBN: (纸本)9781728111056

Standardized visualization of electro- or mechano-anatomical data allows easy inter- and intra-patient comparison. For this purpose, we developed the open-source and freely available UNISYS (Universal Ventricular Bullseye Visualization) software. A patient-specific mesh of the ventricular anatomy typically consists of a certain number of vertices and their associated values. Based on a limited amount of user inputs, the algorithm transforms these 3D single-layer coordinates to a circular 2D disk ('bullseye') through a number of translations and rotations, and interpolates them to achieve a continuous standardized visualization. The algorithm shows a high degree of bidirectionality and a robust spatial preservation of points of interest.

关键词： Visualization Three-dimensional displays Two dimensional displays Software algorithms data visualization Transforms Open source software

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Stacked convolutional sparse auto-encoders for representation learning

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ACM Transactions on knowledge Discovery from data 2021年第2期15卷 31-31页

作者： Zhu, Yi Li, Lei Wu, Xindong School of Information Engineering Yangzhou University China School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods. © 2021 ACM.

关键词： Representation learning Sparse auto-encoder

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