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arXiv 2024年

作者： Li, Siyu Lin, Jiacheng Shi, Hao Zhang, Jiaming Wang, Song Yao, You Li, Zhiyong Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The College of Computer Science Zhejiang University Hangzhou310027 China The USC Viterbi School of Engineering The University of Southern California Los AngelesCA90089 United States

Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://***/lynn-yu/DTCLMapper. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Incentive Mechanism for Throughput Enhancement in Blockchain-based Energy Trading System

Incentive Mechanism for Throughput Enhancement in Blockchain...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Yunshu Liu Man Hon Cheung Jianwei Huang Department of Information Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

In a blockchain-based energy trading system, prosumers engage in energy trading and simultaneously participate in blockchain mining to process trading transactions. However, as blockchain mining consumes energy, prosumers may lack incentives to participate, resulting in a low blockchain throughput and negatively affecting the energy trading market. To address this issue, we propose a Two-Piece Linear Compensation (TPLC) mechanism to incentivize sufficient participation for blockchain mining. We model the mechanism design as a two-stage game, wherein the system designer proposes the mechanism in Stage I, and prosumers decide on their energy allocation strategies in Stage II. We analyze the complex and coupled interactions of prosumers and uncover the reason for the low throughput issue: a self-interested prosumer neglects the positive externality of energy allocation to the blockchain, as it does not benefit him. Despite the diverse objectives of prosumers and the large search space of the mechanism, we demonstrate that the distributed TPLC mechanism achieves the maximum social welfare of a centralized decision benchmark. The numerical analysis compares the TPLC mechanism with linear compensation and no compensation benchmarks. Our results show that the TPLC mechanism achieves the highest social welfare and average payoff for prosumers. Additionally, the TPLC mechanism supports a large energy market without sacrificing the user experience.

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Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning

arXiv

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arXiv 2023年

作者： Wang, Honghui Zhi, Weiming Batista, Gustavo Chandra, Rohitash Transitional Artificial Intelligence Research Group School of Mathematics and Statistics UNSW Sydney Sydney Australia Robotics Institute Carnegie Mellon University Pittsburgh United States School of Computer Science and Engineering UNSW Sydney Sydney Australia

Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilizing prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resulting in a lack of explainability and explicit constraints enforced on predicted trajectories. We present a dynamics-based deep learning framework with a novel asymptotically stable dynamical system integrated into a Transformer-based model. We use an asymptotically stable dynamical system to model human goal-targeted motion by enforcing the human walking trajectory, which converges to a predicted goal position, and to provide the Transformer model with prior knowledge and explainability. Our framework features the Transformer model that works with a goal estimator and dynamical system to learn features from pedestrian motion history. The results show that our framework outperforms prominent models using five benchmark human motion datasets. © 2023, CC BY.

关键词： Dynamical systems

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Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset

arXiv

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arXiv 2025年

作者： Zhang, Yuhong Lin, Jing Zeng, Ailing Wu, Guanlin Lu, Shunlin Fu, Yurong Cai, Yuanhao Zhang, Ruimao Wang, Haoqian Zhang, Lei Shenzhen International Graduate School Tsinghua University Guangdong 518000 China Department of Computer Vision and Robotics IDEA Guangdong 518000 China Whiting School of Engineering Johns Hopkins University MD21218 United States School of Data Science The Chinese University of Hong Kong Guangdong GD Shenzhen518000 China

In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions, and are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue, we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural labels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into Motion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++ provides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K audios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments validate the accuracy of our annotation pipeline and highlight Motion-X++’s significant benefits for generating expressive, precise, and natural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation, audio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Modeling Atomistically Assembled Diffractive Optics in Solids

arXiv

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arXiv 2024年

作者： Kling, Trevor Na, Dong-Yeop Hosseini, Mahdi Department of Electrical and Computer Engineering Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Electrical Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

We develop a model describing long-range atom-atom interactions in a two-dimensional periodic or a-periodic lattice of optical centers inside a solid-state host material. We consider realistic environmental and technical conditions such as frequency and position broadening. Even when considering a significant frequency broadening in the ensemble (∼300 GHz), we observe up to a three-fold increase in directional scattering from the resonant lattice in a system. The model can be used to scalably design quantum optical elements, e.g. a quantum lens, harnessing atomistic engineering (e.g. via ion implantation) of collective interactions in materials to enhance quantum properties. © 2024, CC BY.

关键词： Nanocrystals

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Enhanced Keypoint-Based Approach for Identifying Copy-Move Forgery in Digital Images

Enhanced Keypoint-Based Approach for Identifying Copy-Move F...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Girish Singh Bisht Arpit Jain Vandna Bansla Khemraj Sharma Rudramani Bhutia Vikas Kumar Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Computer Science & Engineering Koneru Lakshamaiah Education Foundation Vadessahwaram A.P. India Shivalik Engineering College Dehradun India Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Chiayi Taiwan R.O.C. Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India School Engineering and Computing Dev Bhoomi Uttrakhand University Dehradun Uttrakhand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of $\mathbf{8 9. 2 \%}, \mathbf{9 3. 9 \%}$ and $\mathbf{9 8. 9 8 \%}$.)

关键词： Accuracy Digital images Forensics Transforms Forgery Computational efficiency Reliability Informatics Software development management

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation

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Procedia computer science 2024年 242卷 1163-1170页

作者： Siyu Zhu Fenfen Zhou Kai Li Haonan Wen Minghao Liu Computer School Beijing information science and technology university 102206 No.55 Taihang Road Changping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University 100101 Beijing China. College of Robotics Beijing Union University 100027 Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University 100871 Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences No.19(A) Yuquan Road Shijingshan District Beijing 100049 P.R. China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments.

关键词： Image retrieval Domain adaptation Semantic segmentation Transfer learning

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Transformer-Based Material Recognition Via Short-Time Contact Sensing

SSRN

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SSRN 2024年

作者： Liu, Zhenyang Shao, Yitian Li, Qiliang Su, Jingyong School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing Beijing100871 China

Embodied intelligence needs haptic sensing for spontaneous and accurate material recognition. The haptic sensing module of an intelligent system can acquire material data through either sliding or tapping motions. Sliding movements are commonly adopted for collecting the spatial frequency features of the material but are less time-efficient than tapping. Here, we introduce a haptic sensing framework that can extract material features from short-time tapping signals. To improve the performance of material recognition, transfer learning is used by transferring the knowledge of pretrained model training on large-scale images into haptic sensing. The waveforms of the tapping signals are encoded as images to be input into a transformer model tailored for image recognition tasks. The encoding employs line graph image-point scaling, effectively accommodating signals that exhibit large variations in magnitude and temporal structures. Using the LMT haptic material database containing sliding and tapping data, our study showcases the efficacy of the proposed framework in material recognition tasks, especially for short-time (≤ 60 ms) sensing via tapping interactions. The findings provide fresh insights into haptic sensing technologies and may help improve the physical interaction capabilities of embodied intelligence, such as medical and rescue robots. © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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