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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

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Virtual Target Based Multi-agent Surrounding Approach

Virtual Target Based Multi-agent Surrounding Approach

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Weiping Zhu Haoyang Li Ning Ding Yukang Chen Yufei Sun Chao Ma Wei Li School of Computer Science Wuhan University P. R. China School of Cyber Science and Engineering Wuhan University P. R. China School of Artificial Intelligence and Computer Science Jiangsu Key Laboratory of Media Design and Software Technology Science Center for Future Foods Jiangnan University P. R. China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Multi-agent surrounding is a collaborative task that uses multiple agents to surround a stationary or moving target. Multi-agent surrounding has a wide range of applications, such as area monitoring of unmanned ships, environmental monitoring, and exploration of unknown environments. Existing work pay attention to the case of one-to-one surrounding of targets by agents, but there is a lack of consideration for the case where agents are not one-to-one with the target. In this paper, we propose the concept of virtual target, which is used as a mediator to realize the generic multi-agent surrounding a target. The main idea is to surround the actual target with the virtual target, while the agents surround the virtual target, where the generation of the virtual target is based on any given surrounding graph and random sampling, and the performance of the surrounding is ensured by the virtual target control algorithm and the agent controlling algorithm. The Lyapunov stability analysis and simulation results show that the proposed approach can make the virtual target fit the actual target effectively, and the agents can surround the actual target with the shape of the virtual target effectively.

关键词： Shape Simulation Fitting Stability analysis Sensors Reliability Environmental monitoring

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High Efficiency Quantum Image Steganography Protocol Based on ZZW Framework 7th

High Efficiency Quantum Image Steganography Protocol Based o...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Sun, Hanrong Qu, Zhiguo School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783030786113

This paper proposes an efficient quantum image steganography protocol based on Zhang Weiming, Zhang Xinpeng and Wang Shuozhong’s steganography framework (ZZW). Based on the high embedding efficiency of the classical ZZW steganography framework, the new protocol combined with the quantum carrier, not only greatly improves the embedding efficiency of the existing quantum image steganography protocols, but also has high security. In order to reflect the practicability of the new protocol, we also designed dedicated quantum circuits to embed and extract secret information. In addition, the simulation results based on Matlab also show that the new protocol not only has good imperceptibility, but also has very high embedding efficiency. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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A Lightweight and Enhanced Semantic Segmentation Network for Mapping of Retrogressive Thaw Slumps from Sentinel-2 Images

A Lightweight and Enhanced Semantic Segmentation Network for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Yuan Guiyun Zhou Zhonghua Su Ziyin Liu Weiwei Sun Xiangchao Meng Jie Wen Zhiqiang Wu School of Resources and Environment University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Sichuan Provincial Public 7 Security Department Communications and Information Technology Headquarters Chengdu China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Fine mapping of retrogressive thaw slumps (RTSs) holds paramount significance in the study of permafrost degradation and carbon exchange. We propose a lightweight and enhanced semantic segmentation network (LessNet) for automatically mapping the RTSs from Sentinel-2 images. LessNet is constructed on the encoder-decoder framework with innovative incorporation of attention mechanism and dual-level semantic features fusion. The lightweight architecture of LessNet eliminates the need for pre-training, and the network hyperparameters are automatically updated based on the training dataset, which allows for fast convergence of supervised learning. Experiments conducted in the Beiluhe region of the Tibetan Plateau highlight the robustness and competitive performance of the model.

关键词： Training Semantic segmentation Supervised learning Transfer learning Semantics Robustness Permafrost

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Research on Resource Scheduling and Optimization Strategies of Edge Computing-based 5G Networks in Smart City Applications 4

Research on Resource Scheduling and Optimization Strategies ...

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4th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2023

作者： Wang, Yongzhe Wang, Dongxu Lin, Guofei Zheng, Bowen Luo, Lanxi Li, Shuai Sichuan University College of Water Resource and Hydropower Sichuan Chengdu China Tongji University No.4800 Caoan Road Jiading Shanghai China Xiamen University Longhai School of Accounting and Finance Jiageng College Fujian Zhangzhou China Hong Kong University of Science and Technology School of Engineering Clear water bay New Territories Sai Kung Hong Kong Minzu University of China Haidian Department of Software Engineering Beijing China Minzu University of China Haidian Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350306323

In this paper, we explore in detail resource scheduling strategies in cloud computing environments. First, we introduce the basic concepts and background of cloud computing, and then delve into the importance and challenges of resource scheduling. The article then describes our proposed new resource scheduling strategy in detail and verifies its performance through experiments. The experimental results show that our strategy outperforms other strategies in terms of task completion time, resource utilization, and the percentage of tasks that satisfy latency requirements. © 2023 IEEE.

关键词： 5G mobile communication systems

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Robust Synthetic-to-Real Transfer for Stereo Matching

Robust Synthetic-to-Real Transfer for Stereo Matching

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiawei Zhang Jiahe Li Lei Huang Xiaohan Yu Lin Gu Jin Zheng Xiao Bai State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Jiangxi Research Institute Beihang University SKLCCSE Institute of Artificial Intelligence Beihang University School of Computing Macquarie University Australia RIKEN AIP The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However, few studies have investigated the robustness after fine-tuning them in real-world scenarios, during which the domain generalization ability can be seriously degraded. In this paper, we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades, but PL preserves the domain generalization ability. Empirically, we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning, consisting of a frozen Teacher, an exponential moving average (EMA) Teacher, and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning. Code is available at: https://***/jiaw-z/DKT-Stereo.

关键词： computer vision Codes Robustness Data models Pattern matching Synthetic data Meteorology

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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EEG-based epileptic seizure pattern decoding using vision transformer

EEG-based epileptic seizure pattern decoding using vision tr...

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International Conference on Innovations in Information Technology

作者： Abdelhadi Hireche Rafat Damseh Parikshat Sirpal Abdelkader Nasreddine Belkacem Department of Computer and Network Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates School of Electrical and Computer Engineering Gallogly College of Engineering University of Oklahoma United States of America

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders.

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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