4.7 Article

Graph convolutional network with triplet attention learning for person re-identification

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Information Systems

Attribute-guided attention and dependency learning for improving person re-identification based on data analysis technology

Heyu Chang et al.

Summary: The paper proposes an improved Re-ID method based on attribute learning, which effectively improves the accuracy of Re-ID by learning fine-grained attribute features and rich dependencies.

ENTERPRISE INFORMATION SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

Deep learning-based person re-identification methods: A survey and outlook of recent works

Zhangqiang Ming et al.

Summary: This paper introduces the research progress in person re-identification (Re-ID) field in recent years, categorizes deep learning-based methods, and discusses the challenges and future research directions in this field.

IMAGE AND VISION COMPUTING (2022)

Article Computer Science, Artificial Intelligence

Cooperative Refinement Learning for domain adaptive person Re-identification

Jinjia Peng et al.

Summary: This paper proposes a Cooperative Refinement Learning (CooRL) framework to address the issues of noisy labels and outlier samples in domain adaptive person re-identification. By developing a multi-branches structure and refinement mechanism, this method can learn more complementary features from pure and noisy samples and optimize the neural network by progressively adjusting the pseudo labels.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Multi-scale local-global architecture for person re-identification

Jing Liu et al.

Summary: This paper proposes an automated framework named multi-scale local-global for person re-identification. The framework includes two components, a high-order attention module to model the subtle differences among pedestrians and generate informative attention features, and a novel architecture called spectral feature transformation to optimize group wise similarities. Experimental results demonstrate the superiority of the proposed method on three benchmark datasets.

SOFT COMPUTING (2022)

Article Computer Science, Artificial Intelligence

A divide-and-unite deep network for person re-identification

Rui Li et al.

Summary: This paper introduces a novel network architecture for discriminative descriptor learning based on global features supplemented by part features. A Feature Division Network is utilized to generate part features with different contributions, ensuring consistency of content between different images.

APPLIED INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Semi-supervised learning for person re-identification based on style-transfer-generated data by CycleGANs

Shangdong Zhu et al.

Summary: This study proposes a semi-supervised learning algorithm for data augmentation using CycleGANs to generate extra unlabeled training data. It incorporates an adaptive receptive field module to expand receptive fields, label smoothing regularization, and an extra class loss to regulate generated data. Three training strategies for the combination of standard dataset and generated samples are proposed to improve performance in person re-identification.

MACHINE VISION AND APPLICATIONS (2021)

Article Computer Science, Information Systems

Cross-view kernel collaborative representation classification for person re-identification

Guoqing Zhang et al.

Summary: Person re-identification (re-ID) faces challenges due to big visual changes of the same individual under different views. Extracting powerful feature representations from pedestrian images is a reasonable solution. The proposed CV-KCRC method aims to find more robust and discriminative feature representations by projecting image features into a common low dimensional subspace, outperforming many state-of-the-art algorithms in experiments on seven commonly used datasets.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

Article Computer Science, Information Systems

Robust person re-identification via graph convolution networks

Guisik Kim et al.

Summary: The research combines graph convolution network with traditional triplet loss methods to adjust the mutual distance between different features through learning, effectively improving the re-identification methods.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Attentive Part-aware Networks for Partial Person Re-identification

Lijuan Huo et al.

Summary: This study introduces a part-aware learning method for partial person re-identification, which innovates in data augmentation and cropping type consistency loss. Experimental results demonstrate that this method achieves state-of-the-art performance in partial re-ID tasks.

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) (2021)

Article Computer Science, Artificial Intelligence

Feature mask network for person re-identification

Guodong Ding et al.

PATTERN RECOGNITION LETTERS (2020)

Article Computer Science, Artificial Intelligence

Effective person re-identification by self-attention model guided feature learning

Yang Li et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Person re-identification with features-based clustering and deep features

Muhammad Fayyaz et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Computer Science, Artificial Intelligence

Scalable Person Re-Identification by Harmonious Attention

Wei Li et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2020)

Article Computer Science, Artificial Intelligence

Similarity learning with joint transfer constraints for person re-identification

Cairong Zhao et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Deep-Person: Learning discriminative deep features for person Re-Identification

Xiang Bai et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression

Yingzhi Tang et al.

NEURAL NETWORKS (2020)

Article Computer Science, Artificial Intelligence

A deep model with combined losses for person re-identification

Di Wu et al.

COGNITIVE SYSTEMS RESEARCH (2019)

Article Computer Science, Information Systems

GLAD: Global-Local-Alignment Descriptor for Scalable Person Re-Identification

Longhui Wei et al.

IEEE TRANSACTIONS ON MULTIMEDIA (2019)

Article Computer Science, Artificial Intelligence

Pose-Invariant Embedding for Deep Person Re-Identification

Liang Zheng et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

Article Engineering, Electrical & Electronic

Pedestrian Alignment Network for Large-scale Person Re-Identification

Zhedong Zheng et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2019)

Article Computer Science, Artificial Intelligence

Person Reidentification by Joint Local Distance Metric and Feature Transformation

Zimo Liu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

Article Computer Science, Artificial Intelligence

Combining multilevel feature extraction and multi-loss learning for person re-identification

Weilin Zhong et al.

NEUROCOMPUTING (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Masked Graph Attention Network for Person Re-identification

Liqiang Bao et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2019) (2019)

Article Computer Science, Artificial Intelligence

Survey on person re-identification based on deep learning

Kejun Wang et al.

CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Harmonious Attention Network for Person Re-Identification

Wei Li et al.

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Level Factorisation Net for Person Re-Identification

Xiaobin Chang et al.

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

DukeMTMC4ReID: A Large-Scale Multi-Camera Person Re-Identification Dataset

Mengran Gou et al.

2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2017)