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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Thakare, Kamalakar Vijay Lohani, Lalit Nayak, Kamakshya Prasad Dogra, Debi Prosad Choi, Heeseung Jung, Hyungjoo Kim, Ig-Jae Indian Institute of Technology Odisha Bhubaneswar752050 India Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul02792 Korea Republic of Yonsei-KIST Convergence Research Institute Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9798331510831

Pedestrian Attribute Recognition (PAR) serves as a fun-damental task in computer vision and is crucial for upgradign security systems. It helps in precisely identifying and characterizing various attributes of pedestrians. However, current PAR datasets have certain issues in representing a wide range of attributes correctly, which makes the ex-isting PAR methods less effective in real-world scenarios. Addressing this limitation, this paper introduces PEARL, a comprehensive dataset comprising of diverse pedestrian images annotated with 146 attributes. These samples have been sourced from surveillance videos across twelve coun-tries. This paper also formulates an image-based PAR using language-image fusion strategy and utilizes CLIP as a new evaluation baseline. Specifically, we leverage textual infor-mation by transforming sets of attributes into meaningful sentences. Addressing the inherent data imbalance in PAR, we provide three types of prompt settings to optimize the training of the CLIP model. Our evaluation encompasses a thorough assessment of the proposed baseline model across various datasets, including PEARL dataset as well as estab-lished PAR benchmarks such as PA100K, RAP, and PETA. © 2025 IEEE.

关键词： Contrastive Learning

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Highly Dynamic Locomotion Control of Biped Robot with Swing Arms 8

Highly Dynamic Locomotion Control of Biped Robot with Swing ...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Wang, Weijie Liu, Song Shan, Qinfeng Jia, Lihao Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350300178

The swinging motion of the arms plays a crucial role in improving the dynamic movement of humans by expanding their ability to control angular momentum, as viewed through the lens of biomechanics. However, biped robot with human morphological structures rarely take full advantage of the swing arms, since there is no effective locomotion control strategy combining swing arms modeling and control reasonably suitable. In this paper, a control strategy is proposed for bipedal robots that incorporates swing arms in the process of achieving jumping motion. The strategy involves modeling, planning trajectories, and tracking movements to enhance the overall performance and stability of the robot. In this process, in order to extract characteristics of swing arms, biped robot is modeled as a flywheel-spring loaded inverted pendulum (F-SLIP), and whole-body controller (WBC) is chosen as the method of trajectory tracking to bring these characteristics into play. Simulation of jumping motion is implemented on a kind of biped robot Purple V1.0 designed for high explosive locomotion, and an well-established evaluation method of biped robot's highly dynamic locomotion is proposed which includes three aspects of agility, stability and energy consumption. Experiment results show this control strategy's effectiveness: agility is increased by a maximum of 6 %, stability is enhanced by an average of 30%, and energy consumption is reduced by an average of 15 %. © 2023 IEEE.

关键词： Energy utilization

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Applying project‐based learning in artificial intelligence and marine discipline:An evaluation study on a robotic sailboat platform

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IET Cyber-Systems and robotics 2022年第2期4卷 86-96页

作者： Xiongwei Lin Hengli Liu Qinbo Sun Xiuhan Li Huihuan Qian Zhenglong Sun Tin Lun Lam The Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong KongShenzhenChina School of Science and Engineering The Chinese University of Hong KongShenzhenChina Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhanChina

intelligence is penetrating various *** demand for interdisciplinary talent is increasingly important,while interdisciplinary educational activities for high school students are lagging ***‐based learning(PBL)in artificial intelligence(AI)and robotic education activities supported by a robotic sailboat platform,the sailboat test arena(STAr),has been shown to popularise AI and robotic knowledge in young *** the implementation of the programme,PBL was provided for students,and gamification pedagogy was applied to increase participants'learning motivation and *** results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school *** assessment results suggest that most students achieve learning outcomes on *** showed more interest in AI and marine disciplines and were willing to participate in more such educational *** findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.

关键词： application platform artificial intelligence educational robots marine robotics robotic game and competition

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Towards Combating Frequency Simplicity-biased Learning for Domain Generalization 38

Towards Combating Frequency Simplicity-biased Learning for D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024 Neural information processing systems foundation. All rights reserved.

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G²VD Planner: Efficient Motion Planning With Grid-Based Generalized Voronoi Diagrams

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IEEE Transactions on Automation Science and Engineering 2024年 22卷 3743-3755页

作者： Jian Wen Xuebo Zhang Qingchen Bi Hui Liu Jing Yuan Yongchun Fang Institute of Robotics and Automatic Information System College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

In this paper, an efficient motion planning approach with grid-based generalized Voronoi diagrams (G2VD) is newly proposed for mobile robots. Different from existing approaches, the novelty of this work is twofold: 1) a new state lattice-based path searching approach is proposed, in which the search space is reduced to a novel Voronoi corridor to further improve the search efficiency; 2) an efficient quadratic programming-based path smoothing approach is presented, wherein the clearance to obstacles is considered to improve the path clearance of hard-constrained path smoothing approaches. We validate the efficiency and smoothness of our approach in various challenging simulation scenarios and outdoor environments. It is shown that the computational efficiency is improved by 17.1% in the path searching stage, and path smoothing with the proposed approach is 6.6 times faster than an advanced sparse-banded structure-based path smoothing approach and 53.3 times faster than the popular timed-elastic-band planner. A video showing outdoor navigation on our campus is available at https://***/iMXGthgvp58 . Note to Practitioners—This paper is motivated by the challenges of motion planning problems of mobile robots. An efficient motion planning approach called G2VD planner is proposed by combining path searching, path smoothing, and time-optimal velocity planning. Extensive simulation and experimental results show the effectiveness of the proposed motion planning approach. However, the prediction information of dynamic obstacles is not incorporated in the proposed motion planner, thus the motion planner may be a bit sluggish in response to dynamic obstacles. Furthermore, we plan to integrate the intention/trajectory prediction of pedestrians/vehicles into the proposed framework to enhance the foreseeability of the motion planner.

关键词： Smoothing methods Planning Robots Search problems Mobile robots Safety Heuristic algorithms

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Out-Bore MRI-Guided Breast Biopsy Employing Machine Vision and Augmented Reality Techniques: Preliminary Study

Out-Bore MRI-Guided Breast Biopsy Employing Machine Vision a...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sangseo Jeon Yeonhyeong Kim Sunghwan Lim Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul Korea

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Magnetic resonance imaging (MRI) of the breast demonstrates superior sensitivity and specificity in distinguishing cancerous tissue. Despite the availability of in-bore MRI-guided breast biopsy, its application is restricted to a limited subset of individuals. We present a novel methodology developed for out-bore MRI-guided breast biopsy. Our approach integrates three primary functionalities: real-time pose tracking of the biopsy needle and a head-mounted display (HMD), real-time shape sensing of the breast with deformable registration, and augmented reality (AR)-assisted visualization of the MRI target and navigational information. Comprehensive experiments were performed to investigate the core performance metrics of the method, including targeting accuracy, update rate of the deformable registration, and data refresh rate and latency of the AR visualization. The mean targeting accuracy was 4.55 ± 1.11mm. The average update rate for deformable registration was 42.76 ± 13.96ms. Furthermore, the data refresh rate and latency associated with AR visualization were recorded as 59 ± 23f ps and 192 ± 73ms, respectively. The experimental findings underscore the system’s promising potential in facilitating successful outcomes for out-bore MRI-guided breast *** relevance— The proposed system facilitates out-bore MRI-guided breast biopsy, enhancing accessibility to MRI-guided breast biopsy for patients. This expansion is anticipated to significantly elevate the detection rate of breast cancer

关键词： Target tracking Shape Navigation Magnetic resonance imaging Data visualization Breast biopsy Resists Needles Real-time systems Sensors

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Quantum Quest: Resource Requirement of RC4 Cryptanalysis under Grover's Lens

Quantum Quest: Resource Requirement of RC4 Cryptanalysis und...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Sen, Sarbani Bajpayee, Soumen Mukherjee, Imon Paul, Goutam Singh, Bhupendra Indian Institute of Information Technology Computer Science & Engineering Kalyani India Indian Statistical Institute Cryptology & Security Research Unit Kolkata India Centre for Artificial Intelligence and Robotics DRDO Bengaluru India

ISBN: (纸本)9798331516666

The RC4 cipher is a widely used software stream cipher which has long stood as a cornerstone in cryptographic applications due to its design simplicity, speed and efficiency. In this paper, we present an approach to decrypt RC4-encrypted data by leveraging Grover's quantum search algorithm. To the best of our knowledge, this is the first documented attempt to employ Grover's algorithm in breaking RC4 encryption. We enumerate the quantum resources required to perform exhaustive key search attack on RC4. Our approach demonstrates the potential of quantum computing to revolutionize cryptanalysis. © 2024 IEEE.

关键词： Encryption algorithms

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Grasp Synergies in Activities of Daily Living: A Cross-sectional Study using a Multi-sensory Data Glove 23

Grasp Synergies in Activities of Daily Living: A Cross-secti...

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6th International Conference on Advances in robotics, AIR 2023

作者： Pratap, Subhash Ito, Kazuaki Hazarika, Shyamanta M. Biomimetic Robotics and Artificial Intelligence Laboratory Indian Institute of Technology Guwahati Assam Guwahati India Ito Laboratory Gifu University Gifu Japan

ISBN: (纸本)9781450399807

Human grasps involved in activities of daily living rely on the correlation of multiple degrees of freedom in a hand. This correlation is called grasp synergies and holds promise for efficient control of robotic hands including prostheses and exoskeletons. A number of studies involving human grasping experiments have been reported. However, the approach, lift and hold phases which are intricately linked to the hand grasp for activities of daily living have hardly been part of such studies. We report a human grasping study which includes the approach, lift and hold phases. Grasp synergies in the force and posture space are identified. We have observed distinct force patterns while manipulating different objects. Additionally, our analysis of correlation coefficients between the pair of fingers highlights the cooperative behaviour and synergy between fingers while grasping. Our study contributes valuable insights into finger correlations, and synergies in terms of grasp postures and forces. © 2023 ACM.

关键词： Degrees of freedom (mechanics)

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A Study on Improving the Performance of Dynamic Model Based Redundant Navigation System for Sensor Fault Scenarios

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Journal of institute of Control, robotics and Systems 2025年第4期31卷 351-359页

作者： Ko, Bo Sung Choi, Hyeong Jun Jeon, Myung Jun Cho, Hyeon Jin Song, Jin Woo Department of Artificial Intelligence and Robotics and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Agency for Defense Development Maritime Technology Research Institute Korea Republic of

This study proposes a redundant navigation system for UUV (Unmanned Underwater Vehicle) utilizing a 6 DOF (Degree of Freedom) VDM (Vehicle Dynamic Model). The system is designed to enable safe UUV asset return when all navigation sensors become inoperable. Since it is impossible to perfectly determine VDM coefficients through water tank experiments, a method to improve VDM output accuracy in real time during UUV mission is necessary. Here in this study, we introduce real-time adaptive 6 DOF VDM output error compensation algorithm to enhance the performance of redundant navigation systems. This research aims to establish the foundation for backup navigation solutions in situations when critical UUV sensor failures occur. © ICROS 2025.

关键词： Water tanks

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MAPTR: STRUCTURED MODELING AND LEARNING FOR ONLINE VECTORIZED HD MAP CONSTRUCTION 11

MAPTR: STRUCTURED MODELING AND LEARNING FOR ONLINE VECTORIZE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Liao, Bencheng Chen, Shaoyu Wang, Xinggang Cheng, Tianheng Zhang, Qian Liu, Wenyu Huang, Chang Institute of Artificial Intelligence Huazhong University of Science & Technology China School of EIC Huazhong University of Science & Technology China Horizon Robotics China

High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. We present MapTR, a structured end-to-end Transformer for efficient online vectorized HD map construction. We propose a unified permutation-equivalent modeling approach, i.e., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process. We design a hierarchical query embedding scheme to flexibly encode structured map information and perform hierarchical bipartite matching for map element learning. MapTR achieves the best performance and efficiency with only camera input among existing vectorized map construction approaches on nuScenes dataset. In particular, MapTR-nano runs at real-time inference speed (25.1 FPS) on RTX 3090, 8× faster than the existing state-of-the-art camera-based method while achieving 5.0 higher mAP. Even compared with the existing state-of-the-art multi-modality method, MapTR-nano achieves 0.7 higher mAP, and MapTR-tiny achieves 13.5 higher mAP and 3× faster inference speed. Abundant qualitative results show that MapTR maintains stable and robust map construction quality in complex and various driving scenes. MapTR is of great application value in autonomous driving. Code and more demos are available at https://***/hustvl/MapTR. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cameras

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