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IEEE Transactions on Artificial intelligence 2024年第7期5卷 3743-3756页

作者： Dong, Nanqing Wang, Zhipeng Sun, Jiahao Kampffmeyer, Michael Knottenbelt, William Xing, Eric Shanghai Artificial Intelligence Laboratory Shanghai200232 China Imperial College London Department of Computing LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom UiT the Arctic University of Norway Department of Physics and Technology Tromso9019 Norway Carnegie Mellon University Machine Learning Department School of Computer Science PittsburghPA15213 United States Mohamed Bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

In the era of deep learning, federated learning (FL) presents a promising approach that allows multiinstitutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most existing FL approaches rely on a centralized server for global model aggregation, leading to a single point of failure. This makes the system vulnerable to malicious attacks when dealing with dishonest clients. In this work, we address this problem by proposing a secure and reliable FL system based on blockchain and distributed ledger technology. Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors. Both theoretical and empirical analyses are presented to demonstrate the effectiveness of the proposed approach, showing that our framework is robust against malicious client-side behaviors. © 2020 IEEE.

关键词： Smart contract

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Enhanced Landmark Detection in Oral and Maxillofacial CT and CBCT Scans: A Multi-Stage Global-Local Integration Approach

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhu, Hailin Li, Xiang He, Tao Liu, Boyan Tang, Wei Guo, Jixiang Sichuan University Machine Intelligence Laboratory College of Computer Science Chengdu610065 China Sichuan University Department of Oral and Maxillofacial Surgery West China Hospital of Stomatology Chengdu610041 China

Cephalometric analysis plays a crucial role in oral clinic, especially in orthodontics and maxillofacial surgery, it is essential for precise and reliable treatment planning and diagnosis. Accurate detection of anatomical landmarks is vital for successful cephalometric analysis. Although significant advancements on deep learning-based landmark detection from medical images have been achieved, especially two-stage coarse-to-fine approaches, the performance remains constrained by the high resolution of images and the consequent limitation of computational resources, particularly with three-dimensional (3D) medical data. In this study, we propose a multi-stage approach to precisely detect landmarks from 3D computed tomography (CT) and cone beam computed tomography (CBCT) images. First, we utilize an alignment network to identify the region of interest and reduce redundant information. Next, we introduce a novel coarse-to-fine detection framework employing 3D U-Net model with heatmap regression as backbones. Specially, we propose a multi-scale global information extraction module (MSGIEM) that combines features from multiple layers with varying resolutions and semantics to capture effective global information. Additionally, a landmark attention module (LAM) is introduced to distill relevant global features for individual landmarks, facilitating their integration with local visual patches to enhance overall detection performance. Extensive experiments on in-house CT and CBCT datasets, as well as a public dataset, demonstrate that the proposed method significantly outperforms state-of-the-art approaches, with a notable reduction on the mean radial error (MRE) by 0.14 mm and 0.04 mm, and an improvement on the successful detection rate (SDR) within 2 mm by 3.98% and 0.23% on in-house CT and CBCT datasets respectively. Furthermore, our discussion on the detection of common landmarks across CT and CBCT datasets underscores its potential clinical applications. © 1963-2012 IEEE

关键词： Maxillofacial prostheses

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Generative Adversarial Network-Based Voice Synthesis from Spectrograms for Low-Resource Speech Recognition in Mismatched Conditions 15

Generative Adversarial Network-Based Voice Synthesis from Sp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bawa, Puneet Kadyan, Virender Chhabra, Gunjan Chitkara University Institute of Engineering & Technology Chitkara University Centre of Excellence for Speech and Multimodal Laboratory Punjab India University of Petroleum & Energy Studies Machine Intelligence Research Centre School of Computer Science Energy Acres Bidholi Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248007 India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350370249

The use of Generative Adversarial Networks (GANs) has been increasing in speech recognition tasks but there has been significant hurdle due to limited availability. The use of GAN have shown promise in speech synthesis tasks, yet their application in low-resource speech systems faces a significant hurdle owing to limited data availability. The progress of effective Automatic Speech Recognition (ASR) systems faces multiple challenges due to a limited range of options and scarcity, resulting in decreased adaptability and efficiency. This article proposes an innovative approach for integrating Generative Adversarial Networks (GANs) to create speech for both adults and children. Experiments have been conducted on using Mel-Spectrograms for synthetic augmentation to address the problem of limited data availability, particularly for low-resource languages and children. The experiments were conducted under both matched and mismatched conditions. The results demonstrate a noteworthy decrease in the Word Error Rate (WER), showcasing the potential of the GAN-based Vocoder model. This leads to an overall Relative Improvement (RI) of 12.74% and 13.95% for the adult and children ASR system, respectively. The research has yielded useful insights on the advancement of ASR systems, particularly in relation to the potential benefits of using GAN-based augmentation in real-world scenarios. © 2024 IEEE.

关键词： Generative Adversarial Network Mismatched Conditions Speech Recognition Vocoder Voice Synthesis

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Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs 38

Neural P3M: A Long-Range Interaction Modeling Enhancer for G...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science United States

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M. © 2024 Neural information processing systems foundation. All rights reserved.

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E²GO: Free Your Hands for Smartphone Interaction

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International Journal of Human-computer Interaction 2025年

作者： Yan, Shaoming Ju, Yuanliang Quan, Rong Tu, Huawei Liang, Dong MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

Current eye-gaze interaction technologies for smartphones are considered inflexible, inaccurate, and power-hungry. These methods typically rely on hand involvement and accomplish partial interactions. In this paper, we propose a novel eye-gaze smartphone interaction method named Event-driven Eye-Gaze Operation ((Formula presented.)), which can realize comprehensive interaction using only eyes and gazes to cover various interaction types. Before the interaction, an anti-jitter gaze estimation method was exploited to stabilize human eye fixation and predict accurate and stable human gaze positions on smartphone screens to further explore refined time-dependent eye-gaze interactions. We also integrated an event-triggering mechanism in (Formula presented.) to significantly decrease its power consumption to deploy on smartphones. We have implemented the prototype of (Formula presented.) on different brands of smartphones and conducted a comprehensive user study to validate its efficacy, demonstrating (Formula presented.) ‘s superior smartphone control capabilities across various scenarios. Demo videos. © 2025 Taylor & Francis Group, LLC.

关键词： Smartphones

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A Deep Learning-Based Approach to Detect Correct Suryanamaskara Pose

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SN computer science 2022年第5期3卷 337页

作者： Bhaumik, Ujjayanta Singh, Koushlendra Kumar Akbari, Akbar Sheikh Bajpai, Manish Kumar University College London United Kingdom Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur India School of Computing Creative Technologies and Engineering at Leeds Beckett University Leeds United Kingdom IIITDM Jabalpur India

We present a technique to analyse Suryanamaskar poses using keypoint estimation and statistical analysis. The proposed approach uses a trained model based on COCO keypoint detection dataset and uses it to determine keypoints in yoga poses. Our work uses the keypoint detection to suggest a self yoga correction system. A novel dataset, Surya-yoga, containing 10000 Suryanamaskara poses has been generated and made publicly available. The model presented in this paper performed better on the COCO dataset and combined COCO and Surya-yoga dataset when tested using part affinity fields. The work also presents an analytical method of distinguishing different Suryanamaskar poses alongside deep learning methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Keypoint detection Pose detection Suryanamaskar Yoga

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Cap4Video++: Enhancing Video Understanding With Auxiliary Captions

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IEEE Transactions on Pattern Analysis and machine intelligence 2024年第7期47卷 5223-5237页

作者： Wenhao Wu Xiaohan Wang Haipeng Luo Jingdong Wang Yi Yang Wanli Ouyang School of Computer Science The University of Sydney Camperdown NSW Australia Stanford University Stanford CA USA University of Chinese Academy of Sciences Beijing China Department of Computer Vision Technology Baidu Inc. Beijing China College of Computer Science and Technology Zhejiang University Zhejiang China Shanghai Artificial Intelligence Laboratory Shanghai China

Understanding videos, especially aligning them with textual data, presents a significant challenge in computer vision. The advent of vision-language models (VLMs) like CLIP has sparked interest in leveraging their capabilities for enhanced video understanding, showing marked advancements in both performance and efficiency. However, current methods often neglect vital user-generated metadata such as video titles. In this paper, we present Cap4Video++, a universal framework that leverages auxiliary captions to enrich video understanding. More recently, we witness the flourishing of large language models (LLMs) like ChatGPT. Cap4Video++ harnesses the synergy of vision-language models (VLMs) and large language models (LLMs) to generate video captions, utilized in three key phases: (i) Input stage employs Semantic Pair Sampling to extract beneficial samples from captions, aiding contrastive learning. (ii) Intermediate stage sees Video-Caption Cross-modal Interaction and Adaptive Caption Selection work together to bolster video and caption representations. (iii) Output stage introduces a Complementary Caption-Text Matching branch, enhancing the primary video branch by improving similarity calculations. Our comprehensive experiments on text-video retrieval and video action recognition across nine benchmarks clearly demonstrate Cap4Video++'s superiority over existing models, highlighting its effectiveness in utilizing automatically generated captions to advance video understanding.

关键词： Streams Computational modeling Visualization Training Semantics computer vision Transformers

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A Robust Detection Network Utilizing Neural Memory Ordinary Differential Equations for Vertebral Fractures Diagnosis

A Robust Detection Network Utilizing Neural Memory Ordinary ...

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Complex Systems and Intelligent science (CSIS-IAC), International Annual Conference on

作者： Saiya Li Litai Ma Dehan Li Yi Deng Yi Yang Lei Xu Machine Intelligence Laboratory West China School of Public Health Sichuan University Chengdu China Department of Orthopedics West China Hospital Sichuan University Chengdu China Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

Clinicians face challenges in diagnosing complex vertebral fractures, relying on a time-consuming manual classification process that hampers standardization. There is an urgent need for automation. Current automated methods encounter significant limitations, including overfitting due to insufficient data, the laborious and subjective nature of data annotation impeding label acquisition, and imbalances in sample numbers compromising classification accuracy across diverse fracture types. To address these challenges, we propose a one-stage, high-accuracy localization, and classification neural network, denoted as DEEP-Spine, empowered by the recent proposed nmODE (neural memory ordinary differential equations). DEEPSpine represents a cutting-edge solution designed to augment nonlinear capabilities, effectively addressing network overfitting and optimizing network training while ensuring computational efficiency. Additionally, we propose an innovative automatic labeling method to alleviate the manual burden on doctors and generate a comprehensive training dataset. Furthermore, we introduce a novel mechanism for constructing a sample set to mitigate the issue of imbalanced sample sizes and enhance classification accuracy. To validate the effectiveness of our approach, we collected CT images of 287 vertebral fracture cases from West China Hospital of Sichuan University and conducted experiments using Intersection over Union (IoU), Accuracy, Precision, Recall, and F1-score metrics on the constructed dataset. The experimental results demonstrate the validity and effectiveness of our model.

关键词： Location awareness Training Measurement Accuracy Neural networks Manuals Standardization Ordinary differential equations Resource management Overfitting

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Self-adaptive image codec for intelligent vision sensor networks

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International Journal of Information Technology (Singapore) 2022年第5期14卷 2533-2540页

作者： Lin, Ming-Yi Chen, Ching-Han Chen, Shou-Cheng Liu, Chao-Tsu Department of Computer Science and Information Engineering Taipei City University of Science and Technology No.2 Xueyuan Rd. Beitou District Taipei 11202 Taiwan Machine Intelligence and Automation Technology (MIAT) Laboratory Department of Computer Science and Information Engineering National Central University No. 300 Jhongda Rd. Jhongli District Taoyuan 32001 Taiwan

Wireless sensor networks (WSNs) play a crucial role in various multimedia services and applications. The main challenge in designing WSNs is coping with hardware resources constraints of vision sensors that involve limited energy, insufficient memory, limited computational capability, and low bandwidth in sensor nodes. In this manuscript, first, WSNs based on self-adaptive protocol was proposed. Next, an embedded processor with hardware resource constrained is applied to make a Joint Photographic Experts Group (JPEG) codec allowing a dynamic range compression ratio for the bandwidth adaptation design of wireless image transmission, which can be used in an un-reliable communication environment. Finally, the system prototype with proposed self-adaptive protocol solution can improve the reliability of real-time image transmission. Experimental results demonstrate our image codec that is more efficient than SPIHT algorithm (Ammah in Inform Med Unlocked 15:100183, 2019). © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Hardware resource-constrained Intelligent vision sensor networks JPEG codec Self-adaptive protocol

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UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments

UnrealROX+: An Improved Tool for Acquiring Synthetic Data fr...

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2021 International Joint Conference on Neural Networks, IJCNN 2021

作者： Martinez-Gonzalez, Pablo Oprea, Sergiu Castro-Vargas, John Alejandro Garcia-Garcia, Alberto Orts-Escolano, Sergio Garcia-Rodriguez, Jose Vincze, Markus University of Alicante Department of Computer Technology Spain University of Alicante Department of Computer Science and Artificial Intelligence Spain Spain Tu Wien Vision for Robotics Laboratory Austria

ISBN: (纸本)9780738133669

Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem. Gathering and labelling the amount of data needed for these data-hungry models in the real world may become unfeasible and error-prone, while synthetic data give us the possibility of generating huge amounts of data with pixel-perfect annotations. However, most synthetic datasets lack from enough realism in their rendered images. In that context UnrealROX generation tool was presented in 2019, allowing to generate highly realistic data, at high resolutions and framerates, with an efficient pipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX enabled robotic vision researchers to generate realistic and visually plausible data with full ground truth for a wide variety of problems such as class and instance semantic segmentation, object detection, depth estimation, visual grasping, and navigation. Nevertheless, its workflow was very tied to generate image sequences from a robotic on-board camera, making hard to generate data for other purposes. In this work, we present UnrealROX+, an improved version of UnrealROX where its decoupled and easy-to-use data acquisition system allows to quickly design and generate data in a much more flexible and customizable way. Moreover, it is packaged as an Unreal plug-in, which makes it more comfortable to use with already existing Unreal projects, and it also includes new features such as generating albedo or a Python API for interacting with the virtual environment from Deep Learning frameworks. © 2021 IEEE.

关键词： Semantics

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