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arXiv 2025年

作者： Zhang, Shengdong Jia, Fan Li, Xiang Zhang, Hao Shi, Jun Ma, Liyan Ying, Shihui Department of Mathematics School of Science Shanghai University Shanghai200444 China Institute University of Utah Salt Lake CityUT84102 United States School of Computer Science and Technology East China Normal University Shanghai China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Shanghai Institute of Applied Mathematics and Mechanics The School of Mechanics and Engineering Science Shanghai University Shanghai200072 China

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMSNet), for the FSS task. Specifically, motivated by the effectiveness of pixel-to-prototype comparison in prototypical FSS methods and the capability of deep priors to model complex spatial structures, we leverage our learned Mumford-Shah model (LMS model) as a mathematical foundation to integrate these insights into a unified framework. By reformulating the LMS model into prototype update and mask update tasks, we propose an alternating optimization algorithm to solve it efficiently. Further, the iterative steps of this algorithm are unfolded into corresponding network modules, resulting in LMS-Net with clear interpretability. Comprehensive experiments on three publicly available medical segmentation datasets verify the effectiveness of our method, demonstrating superior accuracy and robustness in handling complex structures and adapting to challenging segmentation scenarios. These results highlight the potential of LMS-Net to advance FSS in medical imaging applications. Our code will be available at: https:// ***/SDZhang01/LMSNet Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation

arXiv

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arXiv 2023年

作者： Hong, Lingyi Zhang, Wei Gao, Shuyong Lu, Hong Zhang, WenQiang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-stream architectures that separately encode the appearance and motion information before fusing them to identify the target and generate object masks. However, this pipeline is computationally expensive and can lead to suboptimal performance due to the difficulty of fusing the two modalities properly. In this paper, we propose a novel UVOS model called SimulFlow that simultaneously performs feature extraction and target identification, enabling efficient and effective unsupervised video object segmentation. Concretely, we design a novel SimulFlow Attention mechanism to bridege the image and motion by utilizing the flexibility of attention operation, where coarse masks predicted from fused feature at each stage are used to constrain the attention operation within the mask area and exclude the impact of noise. Because of the bidirectional information flow between visual and optical flow features in SimulFlow Attention, no extra hand-designed fusing module is required and we only adopt a light decoder to obtain the final prediction. We evaluate our method on several benchmark datasets and achieve state-of-the-art results. Our proposed approach not only outperforms existing methods but also addresses the computational complexity and fusion difficulties caused by two-stream architectures. Our models achieve 87.4% J &F on DAVIS-16 with the highest speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow also obtains competitive results on video salient object detection datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical flows

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Participatory Design of AI with Children: Reflections on IDC Design Challenge

arXiv

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arXiv 2023年

作者： Bai, Zhen Judd, Frances Polinsky, Naomi Yadollahi, Elmira Department of Computer Science University of Rochester United States Bennett Day School United States Department of Psychology Northwestern University United States Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Children growing up in the era of Artificial Intelligence (AI) will be most impacted by the technology across their life span. Participatory Design (PD) is widely adopted by the Interaction Design and Children (IDC) community, which empowers children to bring their interests, needs, and creativity to the design process of future technologies. While PD has drawn increasing attention to human-centered AI design, it remains largely untapped in facilitating the design process of AI technologies relevant to children and their communities. In this paper, we report intruiguing children’s design ideas on AI technologies resulting from the "Research and Design Challenge" of the 22nd ACM Interaction Design and Children conference (IDC 2023). The diversity of design problems, AI applications and capabilities revealed by the children’s design ideas shed light on the potential of engaging children in PD activities for future AI technologies and education. We discuss opportunities and challenges for accessible and inclusive PD experiences with children. © 2023, CC BY-NC-ND.

关键词： Engineering education

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Innovation-Based Remote State Estimation Secrecy with no Acknowledgments

arXiv

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arXiv 2022年

作者： Kennedy, Justin M. Ford, Jason J. Quevedo, Daniel E. Dressler, Falko School of Electrical Engineering and Robotics Queensland University of Technology 2 George St BrisbaneQLD4000 Australia School of Electrical Engineering and Computer Science TU Berlin Germany

Secrecy encoding for remote state estimation in the presence of adversarial eavesdroppers is a well studied problem. Typical existing secrecy encoding schemes rely on the transmitter’s knowledge of the remote estimator’s current performance. This performance measure is often shared via packet receipt acknowledgments. However, in practical situations the acknowledgment channel may be susceptible to interference from an active adversary, resulting in the secrecy encoding scheme failing. Aiming to achieve a reliable state estimate for a legitimate estimator while ensuring secrecy, we propose a secrecy encoding scheme without the need for packet receipt acknowledgments. Our encoding scheme uses a pre-arranged scheduling sequence established at the transmitter and legitimate receiver. We transmit a packet containing either the state measurement or encoded information for the legitimate user. The encoding makes the packet appear to be the state but is designed to damage an eavesdropper’s estimate. The pre-arranged scheduling sequence and encoding is chosen psuedo-random. We analyze the performance of our encoding scheme against a class of eavesdropper, and show conditions to force the eavesdropper to have an unbounded estimation performance. Further, we provide a numerical illustration and apply our encoding scheme to an application in power systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Transmitters

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Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation

arXiv

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arXiv 2024年

作者： Shi, Long Tang, Chuanqing Deng, Huangyi Xu, Cai Xing, Lei Chen, Badong The School of Computing and Artificial Intelligence China The Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province Southwestern University of Finance and Economics Chengdu611130 China School of Computer Science and Technology Xidian University China The Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster’s combination rule. All these methods only consider inter-view aggregation, yet lacking exploitation of intra-view information. In this paper, we propose a generalized trusted multi-view classification framework with hierarchical opinion aggregation. This hierarchical framework includes a two-phase aggregation process: the intra-view and inter-view aggregation hierarchies. In the intra aggregation, we assume that each view is comprised of common information shared with other views, as well as its specific information. We then aggregate both the common and specific information. This aggregation phase is useful to eliminate the feature noise inherent to view itself, thereby improving the view quality. In the inter-view aggregation, we design an attention mechanism at the evidence level to facilitate opinion aggregation from different views. To the best of our knowledge, this is one of the pioneering efforts to formulate a hierarchical aggregation framework in the trusted multi-view learning domain. Extensive experiments show that our model outperforms some state-of-art trust-related baselines. One can access the source code on https://***/lshi91/GTMC-HOA. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Vivim: a Video Vision Mamba for Ultrasound Video Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yang, Yijun Xing, Zhaohu Yu, Lequan Fu, Huazhu Huang, Chunwang Zhu, Lei Nansha Guangdong Guangzhou511400 China School of Computing and Data Science University of Hong Kong Hong Kong 138632 Singapore Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Department of Ultrasound China Robotics and Autonomous Systems Thrust China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Ultrasound video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are unsatisfactory in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited linear complexity and impressive achievements in efficient long sequence modeling, which have developed deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for ultrasound video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation in ultrasound videos, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. © 1991-2012 IEEE.

关键词： Image segmentation

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CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT for MAPPING UNKNOWN INDOOR ENVIRONMENTS 24

CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT for MAPPING UN...

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2021 24th ISPRS Congress "Imaging Today, Foreseeing Tomorrow", Commission I

作者： Botteghi, N. Schulte, R. Sirmacek, B. Poel, M. Brune, C. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Smart Cities School of Creative Technology Saxion University of Applied Sciences Netherlands Datamanagement and Biometrics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Applied Analysis Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands

Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinforcement learning agent is in charge of selecting the commands for steering the mobile robot, while a SLAM algorithm estimates the robot pose and maps the environments. The agent, to select optimal actions, is trained to be curious about the world. This concept translates into the introduction of a curiosity-driven reward function that encourages the agent to steer the mobile robot towards unknown and unseen areas of the world and the map. We test our approach in explorations challenges in different indoor environments. The agent trained with the proposed reward function outperforms the agents trained with reward functions commonly used in the literature for solving such tasks. © Author(s) 2021.

关键词： Reinforcement learning

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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robotics, Engineering, science, and technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Putting Robots in Context: Challenging the Influence of Voice and Empathic Behaviour on Trust

Putting Robots in Context: Challenging the Influence of Voic...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Marta Romeo Ilaria Torre Sébastien Le Maguer Angelo Cangelosi Iolanda Leite School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh UK School of Computer Science University of Manchester Manchester UK Division of Interaction Design and Software Engineering Chalmers University of Technology Gothenburg Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden ADAPT Centre Trinity College Dublin Dublin Ireland

Trust is essential for social interactions, including those between humans and social artificial agents, such as robots. Several robot-related factors can contribute to the formation of trust. However, previous work has often treated trust as an absolute concept, whereas it is highly context-dependent, and it is possible that some robot-related features will influence trust in some contexts, but not in others. In this paper, we present the results of two video-based online studies aimed at investigating the role of robot voice and empathic behaviour on trust formation in a general context as well as in a task-specific context. We found that voice influences trust in the specific context, with no effect of voice or empathic behaviour in the general context. Thus, context mediated whether robot-related features play a role in people’s trust formation towards robots.

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Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

Multi-Objective Optimization Design for DC-Link Capacitor of...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Qian Liu Kangxing Huang Xueliang Wei Yihong Zheng Hao Xiang School of Automation Wuhan University of Technology Wuhan China Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Hongkong China College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Software Development Department Wuhan iActuator Robotics Co. Ltd Wuhan China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability.

关键词： Solid modeling Costs Capacitors Voltage Permanent magnet motors Capacitance Stability analysis Optimization Thermal stability System analysis and design

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