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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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Efficient Extended Ant Colony Optimization for Capacitated Electric Vehicle Routing

Efficient Extended Ant Colony Optimization for Capacitated E...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Bo-Cheng Lin Xiao-Fang Liu Yi Mei Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9781665487696

With the rapid development of electric vehicle (EV) freight industry, the capacitated electric vehicle routing problem (CEVRP) becomes a challenging and significant prob-lem. CEVRP considers both the cargo capacity of EVs and the battery capacity of EVs, resulting in the difficulty of the construction of feasible solutions. In this paper, we propose an extended ant colony optimization (EACO) algorithm, which uses a pheromone matrix to record the routing information of customers and charging stations for solution construction. Two route construction methods: coarse route construction method and fine route construction method, are developed to generate a customer service sequence with recharging stations. In the coarse route construction method, cargo capacity constraints are ignored and the selection of recharging stations depends on the remaining battery power after the routing of customers only. In the fine routing construction method, the cargo capacities are considered to better evaluate the remaining battery power of vehicles if a return to depots is necessary in the current construction step. After the construction of routing, the giant tour is split and local search is applied to improve solution quality. Experiments show that the proposed EACO is moderate effective. The fine routing construction shows higher exploration ability and performs better than coarse routine construction.

关键词： Industries Ant colony optimization Customer services Charging stations Routing Electric vehicles Batteries

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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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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MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

arXiv

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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 753-765页

作者： Jun Ye Spandan Roy Milinko Godjevac Vasso Reppa Simone Baldi Department of Maritime and Transport Technology Delft University of TechnologyDelft 2628 CDThe Netherlands Robotics Research Center International Institute of Information Technology HyderabadHyderabad-500032India Future Proof Shipping Delft 3013 ALThe Netherlands School of Mathematics and School of Cyber Science and Engineering Southeast UniversityNanjing 210096China the Delft Center for System and Control Delft University of TechnologyDelft 2600 AA

Construction crane vessels make use of dynamic positioning(DP)systems during the installation and removal of offshore structures to maintain the vessel’s *** have reported cases of instability of DP systems during offshore operation caused by uncertainties,such as mooring ***"robustification"for heavy lift operations,i.e.,handling such uncertainties systematically and with stability guarantees,is a long-standing challenge in DP design.A new DP method,composed by an observer and a controller,is proposed to address this challenge,with stability guarantees in the presence of *** test the proposed method on an integrated cranevessel simulation environment,where the integration of several subsystems(winch dynamics,crane forces,thruster dynamics,fuel injection system etc.)allow a realistic validation under a wide set of uncertainties.

关键词： Construction crane vessels dynamic positioning system offshore robust control uncertainty

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A Learning Approach to Multi-robot Task Allocation with Priority Constraints and Uncertainty

A Learning Approach to Multi-robot Task Allocation with Prio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Deng, Fuqin Huang, Huanzhao Fu, Lanhui Yue, Hongwei Zhang, Jianmin Wu, Zexiao Lam, Tin Lun Wuyi University School of Intelligent Manufacturing Guangdong Jiangmen529020 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518000 China The 3irobotix Co. Ltd Guangdong Shenzhen518000 China Guangdong University of Education School of Physics and Information Engineering Guangdong Guangzhou510303 China The Chinese University of HongKong School of Science and Engineering Guangdong Shenzhen518000 China

ISBN: (纸本)9781728119489

Multi-robot task allocation has an important impact on the efficiency of multi-robot collaboration. For single-shot allocation without complicated constraints, some exact algorithms and heuristic algorithms can find the optimal solution efficiently. However, considering the priority constraints and uncertain execution time of robots for multiple times of allocation in an approximate dynamic programming environment, traditional methods such as heuristic algorithms have limited performance. To obtain better performance, we propose a method based on deep reinforcement learning. Specifically, we first use the directed acyclic graph to describe the priority relationship between tasks. Then we propose a graph neural network with a hierarchical attention mechanism to extract the characteristics of the task groups. Finally, we design the policy network to solve the approximate dynamic programming problem of multi-robot task allocation. Through training on the dataset of a given environment, the policy network can gradually refine the decision-making process by reinforcement learning. Experiment results show that the proposed modeling and solving method can find better solutions than existing heuristic algorithms. Furthermore, the learned strategy can be directly applied in other untrained environments with superior performance. © 2022 IEEE.

关键词： Reinforcement learning

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AWNet: Negative obstacle semantic segmentation Network based on adaptive weight loss 22

AWNet: Negative obstacle semantic segmentation Network based...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhao, Jiarui Yu, Guizhen Wang, Jie Wang, Zhangyu Zhang, Junjie Feng, Yunsong School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Beihang University Beijing China Ministry of Industry and Information Technology Beihang University Beijing China Research Institute for Frontier Science Beihang University Hefei China Hefei innovation research institute of Beihang university Beihang University Hefei China State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Heufei China

ISBN: (纸本)9798331527471

On roads, both vehicles and pedestrians will leave traces after passing by. Slight traces will not cause any trouble to the passing vehicles and pedestrians subsequently. However, there are certain potholes in the road that have been formed by repeated crushing of vehicles. It may cause mechanical damage to following vehicles and lead to accidents as vehicles get stuck in the potholes. Thus, the segmentation of negative obstacles is crucial for the safe driving of self-driving vehicles. This paper proposed an AWNet neural network to this end, and quantitative analyses were conducted based on the publicly available dataset DRNO. Specifically, a random cut-and-joint module (RCJ) was proposed to augment the dataset. The cross-entropy loss function was modified by adding an adaptive weight module, i.e., the adaptive weight cross-entropy loss function. It allows the model to focus on negative obstacles during implementation. Furthermore, this paper proposed a post-processing module for adaptive viewpoint conversion based on depth maps, which is capable of fusing depth maps and RGB images to convert the front view into a BEV viewpoint. The mAcc for Negative obstacle semantic segmentation can reach 78.39%, the mIoU can reach 73.21%, the mF1 can reach 79.23%. The experimental results reveal that the AWNet is able to state-of-the-art performance compared with other networks. © 2024 IEEE.

关键词： Semantic Segmentation

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Low-Terahertz Radar Image Analysis for Road Obstacles 33

Low-Terahertz Radar Image Analysis for Road Obstacles

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第33届中国控制与决策会议

作者： Hongming Wu Feng Qi Jinkuan Wang School of communication science and engineering Northeastern UniversityLiaoning Province Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

ISBN: (纸本)9781665431293

At present,laser radar is main studied in the in automotive ***,Driverless cars have higher request for different environment and light *** is even difficult for lidar to find tiny targets on the road,such as pits and *** the paper,The low-terahertz high-resolution imaging radar system operating frequency is 94GHz,Using the frequency-modulated continuous-wave(FMCW) technique with 3 mm *** simulated the pits of different depths on the road,analyzed the changes in the electromagnetic field,and used the low-terahertz radar to perform experiments.

关键词： frequency-modulated continuous-wave(FMCW) low-terahertz radar high-resolution imaging road pit

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