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Real-time instance segmentation based on contour learning

Journal of Measurement Science and Instrumentation 2024年第3期15卷 328-337页

作者： GE Rui LIU Dengfeng ZHOU Haojie CHAI Zhilei WU Qin School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China

Instance segmentation plays an important role in image *** Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial *** solve these problems,a real-time instance segmentation algorithm based on contour learning was ***,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution ***,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more ***,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real ***,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance *** experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti *** proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.

关键词： instance segmentation ShuffleNet V2 lightweight network contour initialization

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Structural Topology Refinement Network for Skeleton-Based Action recognition

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wang, Rui Jin, Jiayao Chen, Ziheng Wu, Cong Wu, Xiao-Jun Sebe, Nicu Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China University of Trento Department of Information Engineering and Computer Science Trento38123 Italy Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence China

The effectiveness of Graph Convolutional Networks (GCNs) has been widely demonstrated in skeleton-based action recognition. However, most existing GCN-based methods use a dense adjacency matrix to describe the structural information of the entire skeleton, i.e., a holistic representation, which neglects the discriminability of local patterns. To address this challenge, we propose a novel region descriptor by dividing the skeleton into different local sections (i.e., left arm, right arm, left leg, right leg, torso, and head). The generated representations contain rich semantic information, enabling the model to better understand the action correlation between different body parts. Inspired by the success of manifold learning in nonlinear data characterization, the Symmetric Positive Definite (SPD) matrix and Riemannian neural network are further introduced to capture the long-range statistical relationships among different topographies. These components form our Structural Topology Refinement Network (STRN). Extensive experiments on three benchmark datasets, namely NTU-60, NTU-120, and NW-UCLA, show the superiority of our proposed method over the state-of-the-art. © 1963-2012 IEEE.

关键词： Musculoskeletal system

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CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection

arXiv

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arXiv 2023年

作者： Wang, Shunfeng Li, Yueyang Luo, Haichi Bi, Chenyang Faculty of Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiang-nan University 1800 Lihu Avenue Jiangsu Wuxi214122 China

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Furthermore, the entire training and inference process is end-to-end. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Multisource and Hidden Source-Based Knowledge Transfer for Solving Dynamic Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Song, Wei Liu, Zhi Yu, Jian Sun, Xiaoyan Jin, Yaochu Lai, Khin Wee Jiangnan University School of Artificial Intelligence and Computer Science Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi214122 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou331712 China Universiti Malaya Department of Biomedical Engineering Kuala Lumpur50603 Malaysia

Recently, transfer learning-based dynamic multiobjective optimization algorithms (TL-DMOAs) have been shown to be very promising in solving dynamic multiobjective optimization problems (DMOPs). However, it is difficult for them to model knowledge capable of delineating the Pareto optimal solutions (POSs) found in each historical environment, because the POSs’ distribution cannot be adequately reflected. Besides, existing TL-DMOAs normally focus on acquiring knowledge from historical environments, but neglect correlations behind them for excavating potential knowledge, restricting the performance in generating high-quality initial populations (HIPs). To address these issues, herein a DMOA with multisource and hidden source-based knowledge transfer (DMOA-MHKT) is proposed. First, we design a knowledge extraction strategy by introducing mean shift, a nonparametric clustering method, to cluster the historical POSs. As clusters’ representatives, the cluster centers are considered to represent environmental knowledge, because they can adequately reflect the POSs’ distribution. Second, the most similar historical environment through environmental match and the last one are selected as two explicit sources. In the former source the POSs’ cluster centers are treated as its knowledge. By contrast, based on the POSs’ cluster centers and knee points in the latter source, a scoring method is designed to generate environmental knowledge by depicting the dynamics between two continuous environments. Third, after aligning knowledge of the explicit sources, a hidden source is learned by excavating correlations and potential knowledge behind them, facilitating the generalization enhancement in generating HIPs. The experimental results especially performance comparisons with seven state-of-the-art DMOAs demonstrate that DMOA-MHKT brings significant improvements in solving DMOPs. © 2025 IEEE. All rights reserved.

关键词： Contrastive Learning

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Hierarchical Knowledge Transfer Network for Distantly Supervised Relation Extraction

Hierarchical Knowledge Transfer Network for Distantly Superv...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wei Song Weishuai Gu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

Distantly supervised relation extraction (DSRE) aims to identify the relation between the two entities (e.g. name and location). Most existing methods extract semantic features from each level separately, without taking into account the transfer of hierarchical knowledge obtained at various levels. As a result, a large amount of knowledge that can improve the quality of the feature representations is lost, resulting in decreased performance for predicting entity relations. In this paper, we propose a novel framework termed the Hierarchical Knowledge Transfer Network (HKTN) that is capable of transferring hierarchical knowledge learned from different levels to improve the performance of predicting entity relations. Specifically, the two representation refinement blocks with re-calibrators at the bag and group levels construct robust bag features and comprehensive group features, respectively. During the construction process, the high-level features are capable of guiding the learning of the bottom-level features using the two re-calibrators. As the construction of the high-level feature representations is based on the bottom-level feature representations, prediction-based contrastive learning fully excavates bottom-level features, which can improve the quality of the feature representation at each level. The experimental results demonstrate that our proposed HKTN achieves an obvious improvement on the two benchmark datasets, including NYT-10 and GDS.

关键词：

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HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model

arXiv

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arXiv 2024年

作者： Shen, Dunbin Zhu, Xuanbing Tian, Jiacheng Liu, Jianjun Du, Zhenrong Wang, Hongyu Ma, Xiaorui The School of Information and Communication Engineering DUT Artificial Intelligence Institute The Dalian Key Laboratory of Artificial Intelligence Dalian University of Technology Dalian116024 China The Jiangsu Provincial Engineering Laboratory for Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, HTD faces challenges due to limited prior knowledge and spectral variations, leading to underfitting models and unreliable performance. To address these challenges, this paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation technique that encodes all surrounding pixels within a patch into a transformed view of the central pixel. Additionally, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Code is available at https://***/shendb2022/HTD-Mamba. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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STRNet:Triple-stream Spatiotemporal Relation Network for Action recognition

引用

International Journal of Automation and computing 2021年第5期18卷 718-730页

作者： Zhi-Wei Xu Xiao-Jun Wu Josef Kittler School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi 214122China Centre for Vision Speech and Signal ProcessingUniversity of SurreyGuildfordGU27XHUK

Learning comprehensive spatiotemporal features is crucial for human action recognition. Existing methods tend to model the spatiotemporal feature blocks in an integrate-separate-integrate form, such as appearance-and-relation network(ARTNet) and spatiotemporal and motion network(STM). However, with blocks stacking up, the rear part of the network has poor interpretability. To avoid this problem, we propose a novel architecture called spatial temporal relation network(STRNet), which can learn explicit information of appearance, motion and especially the temporal relation information. Specifically, our STRNet is constructed by three branches,which separates the features into 1) appearance pathway, to obtain spatial semantics, 2) motion pathway, to reinforce the spatiotemporal feature representation, and 3) relation pathway, to focus on capturing temporal relation details of successive frames and to explore long-term representation dependency. In addition, our STRNet does not just simply merge the multi-branch information, but we apply a flexible and effective strategy to fuse the complementary information from multiple pathways. We evaluate our network on four major action recognition benchmarks: Kinetics-400, UCF-101, HMDB-51, and Something-Something v1, demonstrating that the performance of our STRNet achieves the state-of-the-art result on the UCF-101 and HMDB-51 datasets, as well as a comparable accuracy with the state-of-the-art method on Something-Something v1 and Kinetics-400.

关键词： Action recognition spatiotemporal relation multi-branch fusion long-term representation video classification

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract)

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

引用

International Conference on Data engineering

作者： Wei Fang Haipeng Jiang Hengyang Lu Jun Sun Xiaojun Wu Jerry Chun-Wei Lin Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Distributed Systems and IT Devices Silesian University of Technology Poland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

关键词： Runtime Itemsets Heuristic algorithms Sociology Approximation algorithms Search problems Topology

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Lace Fabric Image Retrieval Using Siamese Neural Network 6

Lace Fabric Image Retrieval Using Siamese Neural Network

引用

6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Xu, Dongdong Li, Yueyang Luo, Haichi Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi China Jiangnan University College of Internet of Things Engineering Wuxi China

ISBN: (纸本)9780738133737

An efficient lace fabric image retrieval method based on DCNN learning features is proposed in this paper. Fine-tuning with Siamese Neural Network is used to learn effective feature of lace fabric image. During the process of training the Siamese Neural Network, hard negative pairs are selective to achieve fast convergence and good performance. The DCNN learning features are combined with the unique shape feature to enable accurate and efficient retrieval of massive image data. Experimental results demonstrate the effectiveness of retrieval performance of the proposed algorithm and possible practical application of the retrieval system in lace fabric industry to improve management efficiency. © 2021 IEEE.

关键词： Content based retrieval

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Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

arXiv

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arXiv 2023年

作者： Bi, Chenyang Li, Yueyang Luo, Haichi Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi China

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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