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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

CAAI Transactions on intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Diffusion Posterior Proximal Sampling for Image Restoration 24

Diffusion Posterior Proximal Sampling for Image Restoration

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Hongjie He, Linchao Zhang, Mingqin Chen, Dongdong Luo, Kunming Luo, Mengting Zhou, Ji-Zhe Chen, Hu Lv, Jiancheng College of Computer Science Sichuan University Chengdu China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China Heriot-Watt University Edinburgh United Kingdom Hong Kong University of Science and Technology Hong Kong Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China College of Computer Science Sichuan University China

ISBN: (纸本)9798400706868

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep. Additionally, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate that our proposed method significantly enhances image restoration performance while consuming negligible additional computational resources. © 2024 ACM.

关键词： Image reconstruction

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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

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RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting...

引用

International Conference on computer vision (ICCV)

作者： Lijun Li Linrui Tian Xindi Zhang Qi Wang Bang Zhang Liefeng Bo Mengyuan Liu Chen Chen Alibaba Group Shanghai Artificial Intelligence Laboratory Key Laboratory of Machine Perception Shenzhen Graduate School Peking University Center for Research in Computer Vision University of Central Florida

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset –RenderIH– for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://***/adwardlee/RenderIH.

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Efficient Multi-Query Oriented Continuous Subgraph Matching 40

Efficient Multi-Query Oriented Continuous Subgraph Matching

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ma, Ziyi Yang, Jianye Zhou, Xu Xiao, Guoqing Wang, Jianhua Yang, Liang Li, Kenli Lin, Xuemin School of Artificial Intelligence Hebei University of Technology China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control China Cyberspace Institute of Advanced Technology Guangzhou University China PengCheng Laboratory Department of New Networks China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (纸本)9798350317152

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings. © 2024 IEEE.

关键词： Directed graphs

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RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

arXiv

引用

arXiv 2023年

作者： Li, Lijun Tian, Linrui Zhang, Xindi Wang, Qi Zhang, Bang Liu, Mengyuan Chen, Chen Alibaba Group China Shanghai Artificial Intelligence Laboratory China Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China Center for Research in Computer Vision University of Central Florida United States

关键词： Textures

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MLP: Motion Label Prior for Temporal Sentence Localization in Untrimmed 3D Human Motions

arXiv

引用

arXiv 2024年

作者： Yan, Sheng Liu, Mengyuan Wang, Yong Liu, Yang Chen, Chen Liu, Hong School of Artificial Intelligence Chongqing University of Technology Chongqing401120 China Key Laboratory of Machine Perception Shenzhen Graduate School Peking University Shenzhen518055 China College of Computer Science Sichuan University Chengdu610065 China Center for Research in Computer Vision University of Central Florida FL32826 United States

In this paper, we address the unexplored question of temporal sentence localization in human motions (TSLM), aiming to locate a target moment from a 3D human motion that semantically corresponds to a text query. Considering that 3D human motions are captured using specialized motion capture devices, motions with only a few joints lack complex scene information like objects and lighting. Due to this character, motion data has low contextual richness and semantic ambiguity between frames, which limits the accuracy of predictions made by current video localization frameworks extended to TSLM to only a rough level. To refine this, we devise two novel label-prior-assisted training schemes: one embed prior knowledge of foreground and background to highlight the localization chances of target moments, and the other forces the originally rough predictions to overlap with the more accurate predictions obtained from the flipped start/end prior label sequences during recovery training. We show that injecting label-prior knowledge into the model is crucial for improving performance at high IoU. In our constructed TSLM benchmark, our model termed MLP achieves a recall of 44.13 at IoU@0.7 on the BABEL dataset and 71.17 on HumanML3D (Restore), outperforming prior works. Finally, we showcase the potential of our approach in corpus-level moment retrieval. Our source code is openly accessible at https://***/eanson023/mlp. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Diffusion Posterior Proximal Sampling for Image Restoration

arXiv

引用

arXiv 2024年

关键词： White noise

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Fast Coding Mode Decision for Intra Prediction in VVC SCC

引用

IEEE Transactions on Broadcasting 2025年第2期71卷 506-516页

作者： Dayong Wang Weihong Liu Zeyu Zhou Xin Lu Jinhua Liu Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Computer Science and Informatics De Montfort University Leicester U.K. School of Digital Technology Industry Shangrao Normal University Shangrao China Glasgow College and the School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Currently, screen content video applications are widely used in our daily lives. As the latest Screen Content Coding (SCC) standard, Versatile Video Coding (VVC) SCC employs a quad-tree plus nested multi-type tree (QTMT) coding structure and various screen content coding modes (CMs). This design enhances the coding efficiency of VVC SCC but also results in a highly complex coding process, which significantly hinders the broader adoption of screen content video technology. Consequently, improving the coding speed of VVC SCC is highly desirable. In this paper, we propose a fast CM and transform decision algorithm for Intra prediction in VVC SCC. Specifically, we initially use Convolutional Neural Networks (CNNs) to predict content types for all Coding Units (CUs). Subsequently, we predict candidate CMs for CUs based on the CM distributions of different content types. We then select the Sum of Absolute Transformed Difference (SATD) as a feature and use a naive Bayes classifier to skip unlikely Intra mode early. Finally, we terminate Block-based Differential Pulse-Code Modulation (BDPCM) early and then select the best transform type in Intra mode prediction to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed by an average of 39.28%, with the BDBR increasing by 0.80%.

关键词： Encoding Copper Prediction algorithms Classification algorithms Transforms Electronic mail Standards Complexity theory Training Partitioning algorithms

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

引用

12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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