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arXiv 2024年

作者： Guo, Jiayi Chen, Zan Ji, Yingrui Zhang, Liyun Luo, Daqin Li, Zhigang Shen, Yiqin Glasgow College University of Electronic Science and Technology of China Chengdu China School of Engineering University of Glasgow Glasgow United Kingdom Toursun Synbio Shanghai China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Research Institute Ubtech Robotics Corp Shenzhen China Qiyuan Lab. Beijing China Department of Computer Science Johns Hopkins University MD United States

Automated Machine Learning (AutoML) can streamline the complexities of machine learning pipelines. However, existing AutoML frameworks focus on discriminative tasks leaving generative tasks unexplored. Moreover, existing AutoML frameworks often lack interpretability and user engagement during the model development and training process, due to the absence of human-centered designs. To address these challenges, we propose UniAutoML, a human-centered AutoML framework that leverages Large Language Models (LLMs) to unify the automation of both discriminative (e.g., Transformers and CNNs for classification or regression) and generative tasks (e.g., fine-tuning diffusion models or LLMs). UniAutoML enables natural language interactions between users and the framework to provide real-time feedback and progress updates, thus enhancing the interpretability, transparency, and user control, where users can seamlessly modify or refine the model training in response to evolving needs. To ensure the safety and reliability of LLM-generated content, UniAutoML incorporates a safety guard-line that filter inputs and censor outputs. We evaluated UniAutoML’s performance and usability through experiments conducted on eight diverse datasets and user studies involving 25 participants. Our results demonstrate that UniAutoML not only achieves higher performance but also improves user control and trust in the AutoML process, which makes ML more accessible and intuitive for a broader audience. The code for UniAutoML is available at: https://***/richarf22/UniAutoML. © 2024, CC BY.

关键词： Digital elevation model

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Collaborative Trolley Transportation System with Autonomous ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Bingyi Xia Hao Luan Ziqi Zhao Xuheng Gao Peijia Xie Anxing Xiao Jiankun Wang Max Q.-H. Meng Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China School of Computing National University of Singapore Singapore Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering Department of Electrical and Computer Engineering Chinese University of Hong Kong in Hong Kong University of Alberta Canada

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video 1 1 Video demonstration: https://***/efnPERm0Rco.)

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

arXiv

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arXiv 2023年

作者： Xia, Bingyi Luan, Hao Zhao, Ziqi Gao, Xuheng Xie, Peijia Xiao, Anxing Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada School of Computing National University of Singapore Singapore

— Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video1) Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning

arXiv

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arXiv 2023年

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha410082 China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha410082 China College of Engineering and Design Hunan Normal University Changsha410082 China

Domain adaptive semantic segmentation enables robust pixel-wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Minimal Solutions for Panoramic Stitching Given Gravity Prior

Minimal Solutions for Panoramic Stitching Given Gravity Prio...

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International Conference on computer Vision (ICCV)

作者： Yaqing Ding Daniel Barath Zuzana Kukelova School of Computer Science and Engineering Nanjing University of Science and Technology Computer Vision and Geometry Group ETH Zürich Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague

ISBN: (纸本)9781665428132

When capturing panoramas, people tend to align their cameras with the vertical axis, i.e., the direction of gravity. Moreover, modern devices, e.g. smartphones and tablets, are equipped with an IMU (Inertial Measurement Unit) that can measure the gravity vector accurately. Using this prior, the y-axes of the cameras can be aligned or assumed to be already aligned, reducing the relative orientation to 1-DOF (degree of freedom). Exploiting this assumption, we propose new minimal solutions to panoramic stitching of images taken by cameras with coinciding optical centers, i.e. undergoing pure rotation. We consider six practical camera configurations, from fully calibrated ones up to a camera with unknown fixed or varying focal length and with or without radial distortion. The solvers are tested both on synthetic scenes, on more than 500k real image pairs from the Sun360 dataset, and from scenes captured by us using two smartphones equipped with IMUs. The new solvers have similar or better accuracy than the state-of-the-art ones and outperform them in terms of processing time.

关键词： Integrated optics Measurement units Optical distortion Cameras Optical imaging Distortion Optical sensors

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Development of an Efficient Charging Control System for Pico-Hydro Power Generation Utilizing Multiple Impact Water Wheel (MIWW) with MPPT Control

Development of an Efficient Charging Control System for Pico...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Yusuke Asami Hiroshi Takami Fuminori Ishibashi Shota Kawate Hidemi Kurita Masaki Shimizu Graduate School of Electrical Eng. and Computer Science Shibaura Institute of Technology Tokyo Japan Dept. of Electrical Eng. and Robotics Course College of Engineering Shibaura Institute of Technology Tokyo Japan Dept. of Electrical Eng. Faculty of Engineering Shibaura Institute of Technology Tokyo Japan Koa Electronics Co. Ltd. Nagano Japan Harmony with Nature Technology Laboratory Nagano Japan Souken Group Nagano Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper introduces an advanced charging control system for a Multiple Impact Water Wheel (MIWW) used in pico-hydropower generation. The control system maximizes energy conversion efficiency by employing a Maximum Power Point Tracking (MPPT) algorithm, which dynamically adjusts the system's operation based on real-time voltage and current measurements. Unlike traditional methods that rely on monitoring rotational speed, this approach simplifies the system by eliminating the need for speed sensors, thereby reducing costs and enhancing reliability. The control system is structured into four main operational phases: the water turbine acceleration phase, optimal control (MPPT) phase, drooping characteristic suppression phase, and overvoltage protection phase. Each phase is designed to optimize distinct aspects of system performance, ensuring stable and efficient power generation under varying water flow conditions. Experimental validation was conducted by subjecting the system to three different water pressure levels (0.036 MPa, 0.048 MPa, and 0.042 MPa). The results demonstrate that the system successfully maintained stable voltage and current, even during rapid fluctuations in water pressure, confirming the effectiveness of the proposed MPPT algorithm. By addressing the challenges of droop characteristics and overvoltage conditions, the control system ensures the reliability and efficiency of pico-hydropower systems. This research contributes to the development of cost-effective, efficient, and sustainable energy solutions, especially in regions lacking conventional power infrastructure.

关键词： Maximum power point trackers Water Fluctuations Heuristic algorithms Velocity control Wheels Sensor systems Voltage control Power generation Turbines

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Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

arXiv

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors

arXiv

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arXiv 2025年

作者： Li, Siyu Cao, Yihong Shi, Hao Zang, Yongsheng He, Xuan Yang, Kailun Li, Zhiyong School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Hunan Vanguard Group Corporation Limited Changsha410100 China

The exploration of Bird’s-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving. BEV mapping models need to be applied to the unlabeled real world, making the study of unsupervised domain adaptation models an essential path. However, research on unsupervised domain adaptation for BEV mapping remains limited and cannot perfectly accommodate all BEV mapping tasks. To address this gap, this paper proposes HierDAMap, a universal and holistic BEV domain adaptation framework with hierarchical perspective priors. Unlike existing research that solely focuses on image-level learning using prior knowledge, this paper explores the guiding role of perspective prior knowledge across three distinct levels: global, sparse, and instance levels. With these priors, HierDA consists of three essential components, including Semantic-Guided Pseudo Supervision (SGPS), Dynamic-Aware Coherence Learning (DACL), and Cross-Domain Frustum Mixing (CDFM). SGPS constrains the cross-domain consistency of perspective feature distribution through pseudo labels generated by vision foundation models in 2D space. To mitigate feature distribution discrepancies caused by spatial variations, DACL employs uncertainty-aware predicted depth as an intermediary to derive dynamic BEV labels from perspective pseudo-labels, thereby constraining the coarse BEV features derived from corresponding perspective features. CDFM, on the other hand, leverages perspective masks of view frustum to mix multi-view perspective images from both domains, which guides cross-domain view transformation and encoding learning through mixed BEV labels. Furthermore, this paper introduces intra-domain feature exchange data augmentation to enhance the efficiency of domain adaptation learning. The proposed method is verified on multiple BEV mapping tasks, such as BEV semantic segmentation, high-definition semantic, and vectorized mapping. It demonstrates competitive perfo

关键词： Birds

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Siamcorners: Siamese corner networks for visual tracking

arXiv

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arXiv 2021年

作者： Yang, Kai He, Zhenyu Pei, Wenjie Zhou, Zikun Li, Xin Yuan, Di Zhang, Haijun School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

The current Siamese network based on region proposal network (RPN) has attracted great attention in visual tracking due to its excellent accuracy and high efficiency. However, the design of the RPN involves the selection of the number, scale, and aspect ratios of anchor boxes, which will affect the applicability and convenience of the model. Furthermore, these anchor boxes require complicated calculations, such as calculating their intersection-over-union (IoU) with ground truth bounding boxes. Due to the problems related to anchor boxes, we propose a simple yet effective anchor-free tracker (named Siamese corner networks, SiamCorners), which is end-to-end trained offline on large-scale image pairs. Specifically, we introduce a modified corner pooling layer to convert the bounding box estimate of the target into a pair of corner predictions (the bottom-right and the top-left corners). By tracking a target as a pair of corners, we avoid the need to design the anchor boxes. This will make the entire tracking algorithm more flexible and simple than anchor-based trackers. In our network design, we further introduce a layer-wise feature aggregation strategy that enables the corner pooling module to predict multiple corners for a tracking target in deep networks. We then introduce a new penalty term that is used to select an optimal tracking box in these candidate corners. Finally, SiamCorners achieves experimental results that are comparable to the state-of-art tracker while maintaining a high running speed. In particular, SiamCorners achieves a 53.7% AUC on NFS30 and a 61.4% AUC on UAV123, while still running at 42 frames per second (FPS). © 2021, CC BY.

关键词： Aspect ratio

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