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Efficient Pre-trained Semantics Refinement for Video Temporal Grounding

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Ao Liu, Huijun Zhu, Yiqing Ge, Yongxin Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Chongqing University College of Computer Science Chongqing401331 China Chongqing University National Elite Institute of Engineering Chongqing401331 China

Video Temporal Grounding (VTG) is a fine-grained video understanding task that aims to ground the relevant video moments corresponding to given language queries. Most existing approaches utilize powerful Vision-Language Models (VLMs), augmented with additional network architectures or specialized modules to supplement temporal reasoning capabilities. Despite achieving impressive performance, these approaches tend to overlook a critical issue that pre-trained visual and textual representations are not specifically optimized for VTG. Particularly, such representations often suffer from inter-modal semantic misalignment referring to the inconsistency between different modalities, and intra-modal semantic confusion, caused by insufficient discriminability within visual modality. To address these limitations, we propose an efficient semantics refinement framework built upon pre-trained models, featuring two core components. First, the Modal Knowledge Bidirectional Propagation (MKBP) component promotes inter-modal semantic alignment via bidirectional enrichment of textual and visual semantics, exploiting their complementary strengths without introducing additional parameters. Second, the Content Context Contrast Learning (C3L) component alleviates intra-modal semantic confusion within visual modality by bringing query-specific visual features closer while separating irrelevant ones. Comprehensive experiments on six benchmark datasets demonstrate the superior performance of our proposed methods. © 1991-2012 IEEE.

关键词： C (programming language)

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Efficient compressed sensing based image coding by using gray transformation

arXiv

引用

arXiv 2021年

作者： Zhang, Bo Xiao, Di Wang, Lan Bai, Sen Yang, Lei Communication NCO Academy Army Engineering University Chongqing400035 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education College of Computer Science Chongqing University Chongqing400044 China School of Mathematics and Information Engineering Chongqing University of Education Chongqing400147 China Chongqing Institute of Engineering Chongqing400056 China

In recent years, compressed sensing (CS) based image coding has become a hot topic in image processing field. However, since the bit depth required for encoding each CS sample is too large, the compression performance of this paradigm is unattractive. To address this issue, a novel CS-based image coding system by using gray transformation is proposed. In the proposed system, we use a gray transformation to preprocess the original image firstly and then use CS to sample the transformed image. Since gray transformation makes the probability distribution of CS samples centralized, the bit depth required for encoding each CS sample is reduced significantly. Consequently, the proposed system can considerably improve the compression performance of CS-based image coding. Simulation results show that the proposed system outperforms the traditional one without using gray transformation in terms of compression performance. © 2021, CC BY-NC-ND.

关键词： Compressed sensing

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Automatic detection of pneumonia in chest X-ray images using cooperative convolutional neural networks 1

引用

2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Wang, Kun Zhang, Xiaohong Huang, Sheng Chen, Feiyu School of Big Data and Software Engineering Chongqing University Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

Chest X-ray images are currently the best available visual media for diagnosing pneumonia, which plays a crucial role in clinical care. Medical images diagnosing can be error-prone for inexperienced radiologists, while tedious and time-consuming for experienced radiologists. To address these issues, we study automatically detect pneumonia in Chest X-ray images. However, this task exists several challenges. First, abnormal regions of pneumonia are difficult to identify due to the noise interference from other tissue and lesions. Second, the features of lung regions are usually essential information for diagnosis. With the pneumonia disease happens in lung areas, only training CNNs using global image may be affected by the irrelevant noisy regions. Third, the appearance of pneumonia in X-ray images is often vague, can overlap with other diagnoses. To cope with these challenges, we first introduce a lung segmentation network, which segments the lung from the original images. Then, develop a feature extraction model, which incorporates global and local features for pneumonia classification. Finally, build a cooperative learning framework, which merges bounding boxes (localization of pneu-monia) from two cooperative models. We demonstrate the effectiveness of our proposed methods on the Kaggles RSNA Pneumonia Detection Challenge dataset and reaches excellent performance with the level of the top 1% in this competition. © Springer Nature Switzerland AG 2019.

关键词： Diagnosis

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MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time

arXiv

引用

arXiv 2020年

作者： Zhou, Xichuan Peng, Yicong Long, Chunqiao Ren, Fengbo Shi, Cong Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Arizona State University TempeAZ United States

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighbouring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25% and 94.74%, respectively. Moreover, the method can realize the real-time image processing at 27.85 FPS, showing promising potential for embedded advanced driving-assistance system applications. Our code is publicly available at https://***/CQUlearningsystemgroup/ YicongPeng. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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SSDKeeper: Self-Adapting Channel Allocation to Improve the Performance of SSD Devices

SSDKeeper: Self-Adapting Channel Allocation to Improve the P...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Renping Liu Xianzhang Chen Yujuan Tan Runyu Zhang Liang Liang Duo Liu Key Lab. of Dependable Service Computing in Cyber Physical Society (Chongqing Univ.) Ministry of Education China School of Microelectronics and Communication Engineering Chongqing University Chongqing China

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Solid state drives (SSDs) have been widely deployed in high performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users' incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that statically allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSDs performance degradation. In this paper, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants to share one SSD. SSDKeeper employs a machine learning assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multi-tenant access patterns and training a model, SSDKeeper selects an optimal channel allocation strategy for multiple tenants with the lowest overall response latency. Experimental results show that SSDKeeper improves the overall performance by 24% with negligible overhead.

关键词： Channel allocation Performance evaluation Hardware Parallel processing Optimization Training Neural networks

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LCL-AKA: Lightweight Authentication and key Agreement Protocol for Power IoT

引用

IEEE Transactions on Smart Grid 2025年

作者： Liu, Zewei Hu, Chunqiang Ruan, Conghao Pu, Yuwen Hu, Pengfei Yu, Jiguo Cyber Physical Society Ministry of Education Key Laboratory of Dependable Service Computing Chongqing University China Chongqing University School of Big Data and Software Engineering Chongqing China China Southern Power Grid Joint Laboratory on Cyberspace Security China Shandong University School of Computer Science and Technology Shandong China Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan250353 China Shandong Laboratory of Computer Networks Jinan250014 China

The rapid adoption of power Internet of Things (PIoT) systems has made security a critical concern, particularly as existing certificateless authentication and key agreement (CL-AKA) protocols face three fundamental limitations that hinder their practical deployment: excessive computational overhead from multi-round interactions, inadequate protection against advanced cryptanalytic attacks, and inflexible architectures unsuitable for dynamic grid environments. To tackle the previously mentioned challenges, this paper proposes lightweight authentication and key agreement protocol (LCL-AKA) for PIoT. Firstly, this protocol efficiently completes the process of identity authentication and session key agreement within a single round of communication interaction, significantly reducing computational costs and improving communication efficiency. Security is rigorously validated through formal proofs under the eCK model and comprehensive analysis showing resistance to key compromise impersonation, unknown key-share attack. Ultimately, comparative experiments are executed to delve into the security and performance characteristics of the proposed protocol. Compared to benchmark protocols deployed in PIoT, the authentication delay of LCL-AKA is decreased by at least 14.88%;in addition, the energy consumption of LCL-AKA is decreased by at least 11.28%. © 2010-2012 IEEE.

关键词： certificateless authentication and key agreement communication efficiency PIoT security proofs

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RFID E-plate Data based Travel Time Reliability Estimation of Urban Expressway

引用

Journal of Physics: Conference Series 2021年第1期1955卷

作者： Siyuan Zhong School of Automation Chongqing University Chongqing 400000 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing

Travel time distribution shows the aggregation degree of vehicle travel time in road network, and travel time reliability is the key index to measure the aggregation degree of travel time. In order to quantitatively evaluate the fluctuation of travel time of expressway vehicles, it is necessary to establish an estimation method of travel time reliability of expressway. Travel time reliability estimation needs to obtain travel time data of vehicles. Traditional Global Position System (GPS) and video detection methods are easily affected by external factors such as weather and environment, and the description of travel time distribution is not comprehensive under the condition of low permeability. On the other hand, in the evaluation of travel time reliability, although there are lots of travel time probability distribution models, the correlation of vehicle travel time between links is ignored when calculating travel time reliability. In order to estimate travel time reliability more accurately, based on Radio Frequency Identification (RFID) electronic license plate (E-plate) data, this paper proposes a path travel time reliability evaluation model considering travel time correlation between links. First, the RFID readers installed on the gantry of expressway acquires data from the electronic tags deployed on vehicles. Secondly, a percentile travel time estimation method is proposed based on Cornish-Fisher expansion and the skewness of travel time distribution. Finally, considering the reliability estimation of travel time under the correlation of links, the estimated value is closer to the true value. The experimental results show that the travel time reliability estimation method considering the correlation between links improves the accuracy of estimation in the expressway network environment.

关键词：

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Secure Cross-Domain Authentication and Data Sharing Scheme for IIoT in Cloud-Fog Automation Architecture

引用

IEEE Journal on Selected Areas in Communications 2025年

作者： Chen, Xi Hu, Chunqiang Cai, Bin Hu, Pengfei Yu, Jiguo Ministry of Education of China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Chongqing University School of Big Data and Software Engineering Chongqing China Shandong University School of Computer Science and Technology Shandong China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Qilu University of Technology Big Data Institute Jinan China

Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security. © 1983-2012 IEEE.

关键词： Sensitive data

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Fractional-order difference curvature-driven fractional anisotropic diffusion equation for image super-resolution

引用

International Journal of Modeling, Simulation, and Scientific computing 2019年第1期10卷 177-189页

作者： Xuehui Yin Shunli Chen Liping Wang Shangbo Zhou School of Software and Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 P.R.China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing 400030P.R.China College of Computer Science Chongqing University Chongqing 400030P.R.China

Image super-resolution methods-based existing edge indicating operators—namely Gauss curvature,mean curvature and gradient-cannot effectively identify the edges,ramps and flat regions and suffer from the loss of fine *** address these issues,this paper presents a fractional anisotropic diffusion equation based on a new edge indicator,named fractional-order difference curvature,which can characterize the intensity variations in *** introduce the frequency-domain definition for fractional-order derivative by the Fourier transform,which is easy to implement *** new edge indicator is better than the existing edge indicating operators in distinguishing between ramps and edges and can better handle the fine *** results for natural images validate that the proposed method can yield a visually pleasing result and better values of MSSIM and PSNR.

关键词： Image super-resolution fractional differentiation difference curvature anisotropic diffusion

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Deep Learning Based Valid Bug Reports Determination and Explanation

Deep Learning Based Valid Bug Reports Determination and Expl...

引用

International Symposium on Software Reliability Engineering (ISSRE)

作者： Jianjun He Ling Xu Yuanrui Fan Zhou Xu Meng Yan Yan Lei Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.

关键词： Fans Computer bugs Semantics Feature extraction Software reliability Task analysis Open source software

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