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Robust Multi-Graph Contrastive Network for Incomplete Multi-View Clustering

IEEE Transactions on Multimedia 2023年 27卷 1-13页

作者： Xue, Zhe Li, Yawen Guan, Zhongchao Li, Wenling Liang, Meiyu Zhou, Hai Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

Food categorization is pivotal in numerous aspects of everyday life, assisting in the selection of food, managing diets, and addressing essential survival requirements. By leveraging the complementary information of various views, multi-view learning usually achieves superior performance compared to the single-view learning methods. However, characterized by the unrestrained openness of internet platforms and potential inconsistencies in food data collection processes, multi-view features often suffer from data loss, resulting in incomplete multi-view food data. Conventional multi-view clustering methods often falter in effectively capitalizing on the diverse correlations contained in food data, and exhibit limitations in dealing with the noise and irregularities pervading different views. Addressing these challenges, this paper presents the Robust Multi-Graph Contrastive network (RMGC) for multi-view food clustering. RMGC artfully combines multi-view representation learning with multi-graph contrastive regularization, creating a cohesive framework to manage incomplete multi-view data. By developing a multi-view encoding network, RMGC seamlessly blends various views into a cohesive representation, astutely assessing the significance of each view. More importantly, the proposed robust multi-graph contrastive regularization enhances the precision of the learned representation and successfully counteracts the noise and unreliability in multi-view data. The experiments conducted across several multi-view datasets manifest the effectiveness of RMGC, showing its superiority over existing methods. Our method not only making an advancement in food categorization but also contributes to the broader field of multi-view learning, offering innovative solutions for handling incomplete and noisy multi-view data. IEEE

关键词： Cluster analysis

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A study on low-dose CT image denoising method based on similar block learning 5

A study on low-dose CT image denoising method based on simil...

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2023 5th International Conference on Information Science, Electrical, and Automation engineering, ISEAE 2023

作者： Fu, Huijuan Xi, Xiaoqi Han, Yu Zhu, Linlin Liu, Mengnan Tan, Siyu Liu, Chang Li, Lei Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing Pla Strategic Support Force Information Engineering University Zhengzhou45000 China Zhengzhou University School of Software Zhengzhou45000 China

ISBN: (纸本)9781510667440

X-ray tomographic imaging has become an important analytical tool with a wide range of applications. It is inevitable that noise is introduced in CT images, and noise reduction is necessary. To solve this problem, we considered to use the nonlocal property of similar block search and proposed a deep learning network based on similar block learning for noise reduction of micro CT short exposure time scanned images to improve the scanning efficiency while ensuring high quality imaging. The method uses the output of the nonlocal method as a data preprocessing algorithm by combining a nonlocal block matching algorithm with a convolutional neural network, and uses a residual channel attention mechanism to learn the features after feature extraction, which reduces noise while preserving image details. Experimental results show that the method can remove noise from CT images quickly and effectively, and compared with the classical CPCE noise reduction method, the method improves the PSNR index by 1.52 dB, which is consistent with the theoretical assumption. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Noise abatement

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Graph-to-Text Generation Combining Directed and Undirected Structural Information in Knowledge Graphs∗ 5

Graph-to-Text Generation Combining Directed and Undirected S...

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5th International Conference on Natural Language Processing, ICNLP 2023

作者： Gong, Hongda Shan, Shimin Wei, Hongkui Dalian University of Technology Software School DaLian116620 China Beijing Institute of Electronic System Engineering State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China

ISBN: (纸本)9798350302219

Graph-to-text generation task is transforms knowledge graphs into natural language. In current research, pretrained language models(PLMs) have shown better performance than structured graph encoders in the generation task. Currently, PLMs serialise knowledge graphs mostly by transforming them into undirected graph structures. The advantage of an undirected graph structure is that it provides a more comprehensive representation of the information in knowledge graph, but it is difficult to capture the dependencies between entities, so the information represented may not be accurate. Therefore, We use four types of positional embedding to obtain both the directed and undirected structure of the knowledge graph, so that we can more fully represent the information in knowledge graph, and the dependencies between entities. We then add a semantic aggregation module to the Transformer layer of PLMs, which is used to obtain a more comprehensive representation of the information in knowledge graph, as well as to capture the dependencies between entities. Thus, our approach combines the advantages of both directed and undirected structural information. In addition, our new approach is more capable of capturing generic knowledge and can show better results with small samples of data. © 2023 IEEE.

关键词： Knowledge graph

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A Burst Topic Detection Model Based on HVCH Model and Burst_NBT Method 4

A Burst Topic Detection Model Based on HVCH Model and Burst_...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Zhang, Chao Peng, Qingcheng Xiang, Huan Liu, Yaowei Guo, Hong Liu, Wenjun Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Computer and Software Engineering Xihua University Chengdu China China Electronics Anshi Technology Co. Ltd Chengdu China

ISBN: (纸本)9798331541729

This paper proposes a burst topic detection model based on HVCH model and Burst_NBT method. The model overcomes the sparsity difficulty of microblog by establishing HVCH model, effectively detects topics of microblog. Then the model establishes Burst_NBT method to calculate the burst scores of the words and cluster the burst words of microblog document sets of each topic, and to score and rank the burst word clusters to detect the corresponding burst topics of each topic. The experiment results indicate that the proposed HVCH-Burst_NBT model improve the evaluation indicators compared with the other two burst topic detection models. © 2024 IEEE.

关键词： burst term similarity calculation topic detection

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An Evaluation Approach for Website Promotion Based on Combined Weight and TOPSIS 22

An Evaluation Approach for Website Promotion Based on Combin...

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5th International Conference on Big Data Technologies, ICBDT 2022

作者： Wang, Xi Chen, Zhichao Kong, Mingming Li, Bo Fan, Mengmeng School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Luzhou China

ISBN: (纸本)9781450396875

In view of the problems existing in the process of website promotion evaluation, such as inadequate decision-making information, strong subjectivity and quantification difficulty of the index weight assignment, and inadequate accuracy of the selected results. For realizing the goal of automatic evaluation for the performance of website promotion, we propose an approach based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) through combined weights. Specifically, based on the construction of a comprehensive evaluation index system for website promotion, the subjective and objective weights are determined by using Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA). Then the combined weights are obtained by Entropy method, and finally TOPSIS is used to rank the performance of website promotion. Experimental results indicate that the ranking results are consistent, and our method can achieve the greatest degree of discrimination. © 2022 ACM.

关键词： Websites

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DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution 43

DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framew...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Mei Jin, Ye Zhu, Jinhui South China University of Technology School of Automation Science and Engineering Guangzhou510641 China South China University of Technology School of Software Engineering Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581581

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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GAN-based Symmetric Embedding Costs Adjustment for Enhancing Image Steganographic Security 24

GAN-based Symmetric Embedding Costs Adjustment for Enhancing...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ye, Miaoxin Zhou, Saixing Luo, Weiqi Tan, Shunquan Huang, Jiwu Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou China Shenzhen Key Laboratory of Media Security College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798400706868

Designing embedding costs is pivotal in modern image steganography. Many studies have shown adjusting symmetric embedding costs to asymmetric ones can enhance steganographic security. However, most existing methods heavily depend on manually defined parameters or rules, limiting security performance improvements. To overcome this limitation, we introduce an advanced GAN-based framework that transitions symmetric costs to asymmetric ones without the need for the manual intervention seen in existing approaches, such as the detailed specification of cost modulation directions and magnitudes. In our framework, we firstly achieve symmetric costs for a cover image, which is randomly split into two sub-images, with part of the secret information embedded into one. Subsequently, we design a GAN model to adjust the embedding costs of the second sub-image to asymmetric, facilitating the secure embedding of the remaining secret information. To support our phased embedding approach, our GAN's discriminator incorporates two steganalyers with different tasks: distinguishing the generator's final output, i.e., the stego image, from both the input cover image and the partially embedded stego image, providing diverse guidance to the generator. In addition, we introduce a simple yet effective update strategy to ensure a stable training process. Comprehensive experiments demonstrate that our method significantly enhances security over existing symmetric steganography techniques, achieving state-of-the-art levels compared to other methods focused on embedding costs adjustments. Additionally, detailed ablation studies validate our approach's effectiveness. © 2024 ACM.

关键词： Steganography

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Knowledge-Guided Cross-Topic Visual Question Generation 30

Knowledge-Guided Cross-Topic Visual Question Generation

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Hongfei Wang, Guohua Xie, Jiayuan Chen, Jiali Fang, Wenhao Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9782493814104

Visual question generation (VQG) task aims to generate high-quality questions based on the input image. Current methods primarily focus on generating questions containing specified content utilizing answers or question types as constraints. However, these constraints make it challenging to control the topic of generated questions (e.g., conversation or test subject topics) for various applications. Thus, it is necessary to utilize topics as constraints to guide question generation. Considering that there are many topics and it is almost impossible for human annotations to cover them, we propose the cross-topic learning VQG (CTL-VQG) task, which aims to generate questions related to unseen topics in cross-topic scenarios. In this paper, we propose a knowledge-guided cross-topic visual question generation (KC-VQG) model to extract unseen topic-related information for question generation. Specifically, an image-topic feature extractor is introduced in our model to extract topic-related intuitive visual features;an image-topic knowledge extractor is used to extract and select the most appropriate topic-related implicit knowledge from large language models for generating questions. Extensive experiments show that our model outperforms baselines and can effectively generate unseen topic-related questions in cross-topic scenarios. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Data mining

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Multi-scale Traffic Camera Image Detection Network Based on Improved YOLOv8 21st

Multi-scale Traffic Camera Image Detection Network Based on...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Peng, Zhihao Qi, Xinyuan Wu, Sheng Shang, Jianga Yang, Linquan China University of Geosciences Wuhan430074 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819601240

With the extensive deployment of traffic cameras across cities, utilizing these devices for vehicle detection has become essential for efficient traffic management. However, variations in vehicle sizes and distances between vehicles and cameras lead to inconsistent scaling of vehicles in the images, which presents a challenge for feature extraction. Furthermore, conventional object detection algorithms fail to fully utilize the rich feature information across multiple layers. To solve these issues, we develop a novel and advanced multi-scale traffic YOLO (MT-YOLO), based on the YOLOv8 model. Firstly, we propose the spatial multi-scale (SMS) attention mechanism to improve the extraction capability of spatial multi-scale vehicle features and aggregate these features for dynamic spatial modulation. Secondly, we develop the multi-scale dilation transposition CARAFE (MDT-CARAFE) upsampling method, which utilizes dilated convolutions to enhance the receptive field at different scales during the upsampling process. Lastly, we augment the network with a detection head and replace the original detection head with the adaptively spatial feature fusion(ASFF) detection head, which dynamically selects and fuses multi-layer feature maps, enabling the network to better capture multi-scale information. Our experiments demonstrate that the MT-YOLO model greatly improves results on the traffic camera detection dataset., achieving an mAP@0.5 of 80.9%, with a precision of 92% and recall of 93%. These results surpass the performance of leading detection algorithms. In addition, the model takes only 12.5 milliseconds to complete an inference, which makes it highly deployable and valuable for real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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