检索结果-内蒙古大学图书馆

Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ER: Equivariance Regularizer for knowledge Graph Completion

arXiv

引用

arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the "size" of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods. © 2022, CC BY.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Geometry Interaction knowledge Graph Embeddings

arXiv

引用

arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean space (zero curved), hyperbolic space (negatively curved) or hyperspherical space (positively curved) to maintain their specific geometric structures (e.g., chain, hierarchy and ring structures). However, the topological structure of KGs appears to be complicated, since it may contain multiple types of geometric structures simultaneously. Therefore, embedding KGs in a single space, no matter the Euclidean space, hyperbolic space or hyperspheric space, cannot capture the complex structures of KGs accurately. To overcome this challenge, we propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. Theoretically, our proposed GIE can capture a richer set of relational information, model key inference patterns, and enable expressive semantic matching across entities. Experimental results on three well-established knowledge graph completion benchmarks show that our GIE achieves the state-of-the-art performance with fewer parameters. © 2022, CC BY.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Hierarchical GAN-Tree and Bi-Directional Capsules for multi-label image classification

引用

knowledge-Based Systems 2022年 238卷

作者： Wang, Boyan Hu, Xuegang Zhang, Chenwei Li, Peipei Yu, Philip S. Hefei University of Technology Anhui Province Hefei China Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Anhui Province Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Anhui Province Hefei China *** Inc. Seattle WA United States University of Illinois at Chicago Chicago IL United States

Compared with the flat multi-label image classification, the hierarchical structure reserves a richer source of structural information to represent complicated relationships between labels in the real world. However, existing multi-label image classification methods focus on the accuracy of label prediction, ignoring the structural information embedded in the hierarchical label space. Furthermore, they hardly form the relevant visual feature space corresponding to the hierarchical label structure. In this paper, we propose a novel hierarchical framework based on the feature and label structural information named Hierarchical GAN-Tree and Bi-Directional Capsules (HGT&BC) to address these problems. We conduct Hierarchical GAN-Tree for feature space representation and Hierarchical Bi-Directional Capsules for label space classification, respectively. Hierarchical GAN-Tree generates hierarchical feature space using the unsupervised divisive clustering pattern according to the hierarchical structure, alleviating the mode-collapse of generators and the overfitting manifestation of conventional GANs. Hierarchical Bi-Directional Capsules utilize the hierarchical label structure in iterations of top-down and bottom-up processes: the top-down process integrates hierarchical relationships into the probability computation to enhance partial hierarchical relationships;the bottom-up process modifies the dynamic routing mechanism between capsules to represent semantic objects for the comprehensive global hierarchical classifiers. Owing to the two components, HGT&BC successfully expresses the hierarchical relationships in both feature and label space and improves the performance of multi-label image classification. Extensive experimental results on four benchmark datasets demonstrate the effectiveness and efficiency of our hierarchical framework in practice. © 2021 Elsevier B.V.

关键词： Capsule Neural Network Generative Adversarial Network Hierarchical learning Multi-label image classification

来源：评论

学校读者我要写书评

暂无评论

Joint item recommendation and attribute inference: An adaptive graph convolutional network approach

arXiv

引用

arXiv 2020年

作者： Wu, Le Yang, Yonghui Zhang, Kun Hong, Richang Fu, Yanjie Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology College of Engineering and Computer Science University of Central Florida

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users’ (items’) characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user (item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user (item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks. To this end, in this paper, we define these two tasks in an attributed user-item bipartite graph, and propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference. The key idea of AGCN is to iteratively perform two parts: 1) Learning graph embedding parameters with previously learned approximated attribute values to facilitate two tasks;2) Sending the approximated updated attribute values back to the attributed graph for better graph embedding learning. Therefore, AGCN could adaptively adjust the graph embedding learning parameters by incorporating both the given attributes and the estimated attribute values, in order to provide weakly supervised information to refine the two tasks. Extensive experimental results on three real-world datasets clearly show the effectiveness of the proposed model. Copyright © 2020, The Authors. All rights reserved.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Learning to transfer graph embeddings for inductive graph based recommendation

arXiv

引用

arXiv 2020年

作者： Wu, Le Yang, Yonghui Chen, Lei Lian, Defu Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology University of Science and Technology of China

With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad applications. As users’ visual preferences are subjective and vary from person to person, previous generalized video highlight extraction models fail to tailor to users’ unique preferences. In this paper, we study the problem of personalized video highlight recommendation with rich visual content. By dividing each video into non-overlapping segments, we formulate the problem as a personalized segment recommendation task with many new segments in the test stage. The key challenges of this problem lie in: the cold-start users with limited video highlight records in the training data and new segments without any user ratings at the test stage. To tackle these challenges, an intuitive idea is to formulate a user-item interaction graph and perform inductive graph neural network based models for better user and item embedding learning. However, the graph embedding models fail to generalize to unseen items as these models rely on the item content feature and item link information for item embedding calculation. To this end, we propose an inductive Graph based Transfer learning framework for personalized video highlight Recommendation (TransGRec). TransGRec is composed of two parts: a graph neural network followed by an item embedding transfer network. Specifically, the graph neural network part exploits the higher-order proximity between users and segments to alleviate the user cold-start problem. The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item’s visual content as input, in order to tackle the new segment problem in the test phase. We design two detailed implementations of the transfer learning optimization function, and we show how the two parts of TransGRec can be efficiently optimized with different transfer learning optimiz

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

LadRa-Net: Locally-aware dynamic re-read attention net for sentence semantic matching

arXiv

引用

arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wu, Le Chen, Enhong Liu, Qi Wang, Meng Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab at Lenovo Research Beijing100094 China University of Science and Technology of China Hefei230026 China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research. © 2021, CC BY.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Multi-Channel Co-Attention Network for Visual Question Answering

Multi-Channel Co-Attention Network for Visual Question Answe...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Weidong Tian Bin He Nanxun Wang Zhongqiu Zhao 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

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Visual Question Answering (VQA) is to reason out correct answers based on input questions and images. Significant progresses have been made by learning rich embedding features from images and questions by bilinear models. Attention mechanisms are widely used to focus on specific visual and textual information in VQA reasoning process. However, most state-of-the-art methods concentrate on fusing the global multi-modal features, while neglect local features. Besides, the dimension is reduced excessively (from K×2048 to 2048) in general visual attention, which causes a mass of visual information loss. In this paper, we propose a novel multi-channel co-attention network (MC-CAN), which integrates multi-modal features from global level to local level. We design different multi-channel attention mechanisms separately for visual (from K×2048 to M×2048) and textual features at different level of integrations. Additionally, we further improve our proposed approach by combining it with the complementary modules such as the MLB and the Count modules. Experiments on benchmark datasets show that our approach achieves better VQA performance than other state-of-the-art methods.

关键词： Visualization Task analysis Feature extraction Correlation knowledge discovery Computational modeling Semantics

来源：评论

学校读者我要写书评

暂无评论

Max-Min Fairness in IRS-Aided MISO Broadcast Channel via Joint Transmit and Reflective Beamforming

Max-Min Fairness in IRS-Aided MISO Broadcast Channel via Joi...

引用

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Caihong Kai Wenqi Ding Wei Huang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) School of Computer Science and Information Engineering Hefei University of Technology Ministry of Education Hefei China

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

The potential application of intelligent reflecting surfaces (IRSs) for future wireless cellular communication systems has motivated the study of metasurface for achieving additional space degree of freedom, where IRS is used to enhance the desired signal strength and suppress the interference. In this paper, by using the additional design degree of freedom provided by the IRS, we jointly optimize the transmit beamforming vector at the BS and the reflective beamforming vector at the IRS to maximize the minimum rate in the IRS-aided multi-user multiple-input-single-output broadcast channel (MISO-BC), subject to the unit modulus constraints of the reflective beamforming vector. In order to solve the non-convex optimization problem, we propose an efficient algorithm based on alternating optimization. In particular, we optimize the transmit beamforming vectors via the second-order cone problem (SOCP) and reflective beamforming vector by using the semi-definite relaxation (SDR). Numerical results show that the use of IRS leads to significant higher SINR values than benchmark schemes without IRS.

关键词： Wireless communication Array signal processing Transmitters Interference MISO communication Optimization Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：