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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Yulin Sun Chao Zhang Haonan Xu Qunqin Pan School of Computer Science and Technology Changchun University of Science and Technology Changchun China National & Local Joint Engineering Research Center for Special Film Technology and Equipment Changchun China

Facial landmark detection is the basis of face analysis, which is of great significance. Aiming at the lack of accuracy and robustness of facial landmark detection in difficult scenes, we propose an adaptive facial landmark detection network Adaptively-3FabRec. In order to avoid the loss of key semantic information and reduce the accuracy, we introduce an adaptive spatial feature fusion module, which can adaptively fuse key features; in order to improve the accuracy of key points, Adaptive wing loss is introduced as a supplement to make the network focus on foreground pixels and difficult background pixels. Under the same experimental settings, the experimental results on the 300-W data set show that the NME, FR and AUC value of the Adaptively-3FabRec are 3.96,0.87 and 60.32, respectively, which are 9.3 %, 23.5 % and 1.8 % higher than the baseline 3FabRec.

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Quantum Error Correction Based Entanglement Routing in Socially-Aware Quantum Networks

Quantum Error Correction Based Entanglement Routing in Socia...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shao-Min Huang Ming-Huang Chien Ting-Yuan Wen Qian-Jing Wang Jian-Jhih Kuo Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Quantum teleportation enables high-security communications via quantum entanglement. However, decoherence, signal decay, and environmental interference may cause imperfect entangled pairs with low fidelity. Such entangled pairs may easily generate errors when used to teleport data qubits and cause fatal computation errors on quantum computers. Fortunately, data qubits can be encoded via quantum error correction (QEC) code to recover the detected error to some extent. Nevertheless, letting any repeaters process data qubits is dangerous because malicious repeaters may peep at, destroy, or fake the data qubits. Thus, in this paper, we propose a novel QEC-enabled routing framework MOON facilitated with the concept of social networks (SNs) for quantum networks (QNs). MOON selects only trusted repeaters to process data qubits and exhibits elastic routing with QEC to maximize the throughput without errors. Last, simulation results show that our framework can outperform existing approaches by up to 34% on network throughput without errors.

关键词： Quantum entanglement Social networking (online) Simulation Qubit Moon Teleportation Repeaters Throughput Routing Quantum networks

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Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models

arXiv

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arXiv 2024年

作者： Liu, Yuanwei Jia, Chengyu Xiao, Ruqi Jia, Xuemei Wei, Hui Jiang, Kui Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin China

The task of privacy-preserving face recognition (PPFR) currently faces two major unsolved challenges: (1) existing methods are typically effective only on specific face recognition models and struggle to generalize to black-box face recognition models;(2) current methods employ data-driven reversible representation encoding for privacy protection, making them susceptible to adversarial learning and reconstruction of the original image. We observe that face recognition models primarily rely on local features (e.g., face contour, skin texture, and so on) for identification. Thus, by disrupting global features while enhancing local features, we achieve effective recognition even in black-box environments. Additionally, to prevent adversarial models from learning and reversing the anonymization process, we adopt an adversarial learning-based approach with irreversible stochastic injection to ensure the stochastic nature of the anonymization. Experimental results demonstrate that our method achieves an average recognition accuracy of 94.21% on black-box models, outperforming existing methods in both privacy protection and anti-reconstruction capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Authorizable Tripartite Entanglement Routing via 3-GHZ State in Quantum Networks

Authorizable Tripartite Entanglement Routing via 3-GHZ State...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shao-Min Huang Ching-Ting Wei Kai-Xu Zhan Juliette Chou Le Touze Jian-Jhih Kuo Chih-Yu Wang Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Traditional end-to-end entanglement typically operates between two parties. However, such a setup may fall short when three parties are involved. GHZ states, a multi-qubit entangled state, provide an approach to such a problem. A fusion node is first chosen, and then the paths from all end nodes to the fusion node are fused to create an entangled state between all parties. The selection of the fusion node becomes critical since it highly affects the success probability of the whole process. Thus, in this paper, we explore the promising scenario for multiple requests of authorizable tripartite teleportation and introduce a novel optimization problem to maximize the (expected) total profit for 3-GHZ requests. Via extensive simulation results, we show that our algorithm can outperform existing approaches significantly.

关键词： Quantum entanglement Simulation Teleportation Routing Quantum networks Global communication Optimization

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Near-Optimal Swapping and Purifying Strategy for All-Optical-Switching Entanglement Routing

Near-Optimal Swapping and Purifying Strategy for All-Optical...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shao-Min Huang Tang-Ming Hsu Jing-Jhih Du Jian-Jhih Kuo Chih-Yu Wang Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Entangled pairs serve as the cornerstone for secure data transmission. All-optical-switching technology on nodes enables the entangling signals to bypass nodes and build ultra-long entangled pairs. Nevertheless, entangled pairs suffer from decoherence over distance, causing inadequate fidelity and potentially compromising transmission quality. To address the challenges, we employ entanglement purification to enhance fidelity to meet the threshold. However, the purification process consumes additional entangled pairs and may fail. Besides, the purification efficiency would be poor if the input pairs have low fidelity. Thus, it is unavoidable to divide the path into sub-paths with appropriate lengths for better purification efficiency and then merge them into a longer entangled pair by swapping. The novel optimization problem DOSP then emerges: maximizing the probability while adhering to fidelity constraints. To tackle the DOSP efficiently, we propose a (1–δ)-approximation algorithm NSPS to consider the probability and fidelity jointly, where is a positive user-defined constant. Finally, the simulation results manifest that the NSPS can outperform the existing methods by at least 70%.

关键词： Purification Simulation Memory management Routing Global communication Data communication Optimization

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Obstacle Avoidance Path Planning for Robotic Arm Based on EIT Tactile Sensing

Obstacle Avoidance Path Planning for Robotic Arm Based on EI...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Kai Meng Wendong Zheng Ling Weng Huaping Liu School of Electrical Engineering Hebei University of Technology Tianjin China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

This study introduces a novel flexible tactile sensor using electrical impedance tomography (EIT) for tactile sensing and integrates it into a robotic arm to enable it to have tactile sensing capabilities. In order to enable the robot to autonomously perform interactive operations in an unstructured open environment, this paper proposes a path planning algorithm based on the artificial potential field method in combination with the above-mentioned tactile sensor to achieve autonomous obstacle avoidance of the robotic arm. Specifically, a repulsive potential field is proposed, which is adjusted according to the height of the robotic arm to guide it around detected obstacles. In order to verify the effectiveness of the proposed flexible tactile sensor and obstacle avoidance path planning strategy, the designed tactile sensor and obstacle avoidance algorithm were integrated into the UR5 robotic arm platform for experimental verification. The experimental results show that the robotic arm can accurately detect tactile targets in the environment, and can autonomously adjust the motion path in real time according to the environment, realizing autonomous and safe interaction of the robotic arm in actual scenarios. This study helps to improve the autonomy and operational safety of robots in complex environments, and has important theoretical and practical significance in the field of robot tactile perception and obstacle avoidance navigation.

关键词： Electrical impedance tomography Shape Tactile sensors Arms Manipulators Reliability engineering Sensors

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CMANet: A CNN-Mamba aggregation network for face super-resolution

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Pattern Recognition 2025年 168卷

作者： Wu, Ziyi Lu, Tao Zhang, Yanduo Chai, Xiaoyu Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Computer School Hubei University of Arts and Science Xiangyang China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

Currently, most face super-resolution (FSR) methods rely on convolutional neural networks (CNNs) or Transformers, both of which have inherent limitations: CNNs suffer from a limited receptive field, while Transformers introduce quadratic computational complexity due to self-attention. In recent years, Mamba has effectively addressed these challenges using the state space model (SSM), excelling in global modeling. However, due to the highly structured nature of face images, relying solely on global modeling is insufficient for high-quality reconstruction. It is crucial to preserve global structural information while enhancing local texture details. While Mamba excels in global modeling, it lacks the ability to effectively extract local details. To eliminate this issue, we propose the CNN-Mamba Aggregation Network (CMANet) for FSR, which consists of two branches: a CNN branch and a Mamba branch. The CNN branch is a traditional end-to-end FSR network, in which the proposed Multi-Scale Residual Block (MSRB) extracts multi-scale local details. The Mamba branch, the core of the network, is built upon a UNet architecture. Its central component is a carefully designed Dual Scan Mamba Block (DSMB), which combines the Dual Scan Mamba Module (DSMM) and Feature Refinement Feed-Forward Network (FR-FFN). The DSMM enhances the global modeling capabilities of the SSM in both plane and channel scanning directions, while the FR-FFN refines the expressiveness and adaptability of the features. Finally, CMANet reconstructs high-fidelity face images by aggregating features from both branches. Extensive experimental results show that our model significantly outperforms many advanced methods in both objective metrics and subjective image quality. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Turning rough surfaces into non-line-of-sight cameras

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Optica 2025年第5期12卷 626-634页

作者： Li, Wenwen Zhou, Yijun Li, Wei Chen, Yutao Huang, Xin Dai, Chen Zeng, Jianwei Goyal, Vivek K. Xu, Feihu Pan, Jian-Wei Hefei National Research Center for Physical Sciences the Microscale School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Electrical and Computer Engineering Boston University 8 St. Mary’s Street BostonMA02215 United States

Overcoming the spatial mixing of reflection and scattering from rough surfaces to see a hidden scene is a difficult challenge with a wide range of applications. Existing methods counter the mixing effect using active time-of-flight or coherence-based measurements but at the cost of expensive and dedicated optical systems. Here, we derive a model to accurately describe the microscale scattering properties of a rough surface and show it is well-conditioned for inversion. This enables computational passive non-line-of-sight (NLOS) imaging with unprecedented capabilities. With an ordinary monochrome camera, we demonstrate real-time and high-resolution passive NLOS imaging with spatial resolution of sub-mm, temporal resolution of 25 fps, field of view of near 90◦, and full-color recovery. Furthermore, our approach permits high-quality keyhole NLOS imaging through a mm-scale keyhole and, notably, allows for non-invasive calibration and imaging without any manipulation of the hidden scene. These results open a new avenue for the development ofNLOS imaging, scattering imaging, and relevant applications to real-world conditions. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Image resolution

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS 12

VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GRO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center Turkey Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies Italian National Council of Research Italy Hacettepe University Department of Computer Engineering Turkey Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computational linguistics

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Encrypted Domain Secret Medical-Image Sharing with Secure Outsourcing Computation in IoT Environment

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IEEE Internet of Things Journal 2024年第2期11卷 2382-2393页

作者： Huang, Jingwang Cui, Qi Zhou, Zhili Yu, Keping Yang, Ching-Nung Choo, Kim-Kwang Raymond Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Nanjing University Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanyang Technological University School of Electrical and Electronic Engineering Jurong West639798 Singapore Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

In existing secret medical-image sharing (SMIS) schemes, to protect and manage secret medical-images (SMIs), the sharing and recovery of each SMI are implemented by local servers of medical institutions. However, since a lot of SMIs are produced by personal smart terminal devices in Internet of Things (IoT) environment, directly implementing the sharing and recovery processes will cause excessive communication and computing burden for those local servers, which makes the existing SMIS schemes not suitable for IoT environment. To address the above issue, we propose an encrypted domain SMIS (Enc-SMIS) scheme with secure outsourcing computation for protecting and managing medical images in IoT environment. In the proposed scheme, the medical images are first encrypted using fully homomorphic encryption (FHE) and then outsourced to a cloud server for generating a set of image shares. Subsequently, these shares are stored separately in different local servers of medical institutions. Furthermore, the recovery process is also outsourced to the cloud server when doctors need to observe the patients' medical images. Compared with the existing SMIS schemes, the proposed Enc-SMIS scheme alleviates the computing and communication burden on local servers significantly with secure outsourcing computation in the semi-honest model, and thus supports the storage and management of medical images well in IoT environment. © 2014 IEEE.

关键词： Internet of things

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