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A Modified Memetic Algorithm for Multi-depot Green Capacitated Arc Routing Problem 14th

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A Modified Memetic Algorithm for Multi-depot Green Capacitat...

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Cao, Bin Li, Ruichang Chen, Shuai Hebei University of Technology Tianjin300401 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi’an710071 China Hebei Provincial Key Laboratory of Big Data Calculation Tianjin300401 China

ISBN: (纸本)9789811534249

The capacitated arc routing problem (CARP) has important research significance in the area of vehicle routing services. We study a multi-depot green capacitated arc routing problem by minimizing economic cost, makespan and carbon emission cost, and then, present a modified memetic algorithm for multi-depot (MMAMD) that aims to solve the problem efficiently. According to the characteristics of multi-depots in this paper, a specific encoding scheme via the two-dimensional array and the dynamic adjustment strategy of boundary arcs between depots are proposed. By combining ant colony algorithm with local search, we present an ant colony local search strategy with extended neighborhood search for the memetic algorithm. The experimental results show that the proposed algorithm which is used to solve the model is effective, which can provide a reference for waste clearing decision. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Vehicle routing

Multi-objective Pick-up Point Location Optimization Based on a Modified Genetic Algorithm 14th

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Multi-objective Pick-up Point Location Optimization Based on...

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Chen, Shuai Cao, Bin Li, Ruichang Hebei University of Technology Tianjin300401 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi’an710071 China Hebei Provincial Key Laboratory of Big Data Calculation Tianjin300401 China

ISBN: (纸本)9789811534249

To solve the logistics terminal distribution problem, we construct the multi-objective location allocation model of pick-up point. The model considers the characteristics of express logistics system, enterprise demand and customer demand for delivery distance, and two objectives of minimizing location cost and maximizing customer distance satisfaction are included. We set the segmentation distance function according to the distance of the user from the pick-up point, and calculate the satisfaction function. To solve the model, we modify the non-dominated sorting genetic algorithm with elite strategy algorithm (NSGA-II). Consider the needs of the enterprise or user, we modify the crossover operator, and use the tournament method to evaluate the offspring population generated by genetic operations to reduce the loss of elite individuals. We also verify the effectiveness of the model and the modified algorithm through experiments. Experimental results show that the modified algorithm can obtain a better Pareto optimal solution set for the multi-objective location-allocation problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Location

Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

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arXiv 2022年

作者： Han, Yuehui Hui, Le Jiang, Haobo Qian, Jianjun Xie, Jin Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local structures of the graph. Also, the vector inner product based similarity metric cannot fully exploit the local structures of the graph to characterize the graph difference well. To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs. It aims to generate contrastive samples by capturing the intrinsic structures of the graph and distinguish the samples based on the features and structures of subgraphs simultaneously. Specifically, for each center node, by adaptively learning relation weights to the nodes of the corresponding neighborhood, we first develop a network to generate the interpolated subgraph. We then construct the positive and negative pairs of subgraphs from the same and different nodes, respectively. Finally, we employ two types of optimal transport distances (i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the structured contrastive loss. Extensive node classification experiments on benchmark datasets verify the effectiveness of our graph contrastive learning method. Source code is available at https://***/yh-han/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

Learning superpoint graph cut for 3D instance segmentation 22

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Learning superpoint graph cut for 3D instance segmentation

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Le Hui Linghua Tang Yaqi Shen Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781713871088

3D instance segmentation is a challenging task due to the complex local geometric structures of objects in point clouds. In this paper, we propose a learning-based superpoint graph cut method that explicitly learns the local geometric structures of the point cloud for 3D instance segmentation. Specifically, we first oversegment the raw point clouds into superpoints and construct the superpoint graph. Then, we propose an edge score prediction network to predict the edge scores of the superpoint graph, where the similarity vectors of two adjacent nodes learned through cross-graph attention in the coordinate and feature spaces are used for regressing edge scores. By forcing two adjacent nodes of the same instance to be close to the instance center in the coordinate and feature spaces, we formulate a geometry-aware edge loss to train the edge score prediction network. Finally, we develop a superpoint graph cut network that employs the learned edge scores and the predicted semantic classes of nodes to generate instances, where bilateral graph attention is proposed to extract discriminative features on both the coordinate and feature spaces for predicting semantic labels and scores of instances. Extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, show that our method achieves new state-of-the-art performance on 3D instance segmentation.

关键词：

3D Siamese Transformer Network for Single Object Tracking on Point Clouds

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arXiv 2022年

作者： Hui, Le Wang, Lingpeng Tang, Linghua Lan, Kaihao Xie, Jin Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance variation between the template and search area during tracking, how to learn the robust cross correlation between them for identifying the potential target in the search area is still a challenging problem. In this paper, we explicitly use Transformer to form a 3D Siamese Transformer network for learning robust cross correlation between the template and the search area of point clouds. Specifically, we develop a Siamese point Transformer network to learn shape context information of the target. Its encoder uses self-attention to capture non-local information of point clouds to characterize the shape information of the object, and the decoder utilizes cross-attention to upsample discriminative point features. After that, we develop an iterative coarse-to-fine correlation network to learn the robust cross correlation between the template and the search area. It formulates the cross-feature augmentation to associate the template with the potential target in the search area via cross attention. To further enhance the potential target, it employs the ego-feature augmentation that applies self-attention to the local k-NN graph of the feature space to aggregate target features. Experiments on the KITTI, nuScenes, and Waymo datasets show that our method achieves state-of-the-art performance on the 3D single object tracking task. Source code is available at https://***/fpthink/STNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Learned interpretable residual extragradient ISTA for sparse coding

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arXiv 2021年

作者： Kong, Lin Sun, Wei Shang, Fanhua Liu, Yuanyuan Liu, Hongying Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China Peng Cheng Laboratory Shenzhen China

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a residual structure and theoretical guarantees. In particular, our algorithm can also provide the interpretability for Res-Net to a certain extent. From a theoretical perspective, we prove that our method attains linear convergence. In practice, extensive empirical results verify the advantages of our method. © 2021, CC BY.

关键词： Iterative methods

Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention

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arXiv 2024年

作者： Dai, Yimian Pan, Peiwen Qian, Yulei Li, Yuxuan Li, Xiang Yang, Jian Wang, Huan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security China The department of intelligence science School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanjing Marine Radar Institute Nanjing China VCIP CS Nankai University China The NKIARI Futian Shenzhen China

Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detection precision and false alarm rates. To break this dilemma, we propose SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the "Pick of the Bunch" principle. At its core lies our Selective Rank-Aware Attention (SeRank) module, employing a non-linear Top-K selection process that preserves the most salient responses, preventing target signal dilution while maintaining constant complexity. Furthermore, we replace the static concatenation typical in U-Net structures with our Large Selective Feature Fusion (LSFF) module, a dynamic fusion strategy that empowers SeRankDet with adaptive feature integration, enhancing its ability to discriminate true targets from false alarms. The network’s discernment is further refined by our Dilated Difference Convolution (DDC) module, which merges differential convolution aimed at amplifying subtle target characteristics with dilated convolution to expand the receptive field, thereby substantially improving target-background separation. Despite its lightweight architecture, the proposed SeRankDet sets new benchmarks in state-of-the-art performance across multiple public datasets. The code is available at https://***/GrokCV/SeRankDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

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arXiv 2023年

作者： Jiang, Haobo Salzmann, Mathieu Dang, Zheng Xie, Jin Yang, Jian PCA Lab Nanjing University of Science and Technology China CVLab EPFL Switzerland ClearSpace Switzerland PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud to obtain a precise alignment with the model point cloud. Training our framework involves two operations: An SE(3) diffusion process and an SE(3) reverse process. The SE(3) diffusion process gradually perturbs the optimal rigid transformation of a pair of point clouds by continuously injecting noise (perturbation transformation). By contrast, the SE(3) reverse process focuses on learning a denoising network that refines the noisy transformation step-by-step, bringing it closer to the optimal transformation for accurate pose estimation. Unlike standard diffusion models used in linear Euclidean spaces, our diffusion model operates on the SE(3) manifold. This requires exploiting the linear Lie algebra se(3) associated with SE(3) to constrain the transformation transitions during the diffusion and reverse processes. Additionally, to effectively train our denoising network, we derive a registration-specific variational lower bound as the optimization objective for model learning. Furthermore, we show that our denoising network can be constructed with a surrogate registration model, making our approach applicable to different deep registration networks. Extensive experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets. Code is available at https://***/JiangHB/DiffusionReg. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

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arXiv 2022年

作者： Shen, Yaqi Hui, Le Jiang, Haobo Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation. Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss, and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance. Our code is available at https://***/supersyq/RIENet. Copyright © 2022, The Authors. All rights

关键词： Surface measurement