Proceedings ArticleDOI
Unsupervised Graph Association for Person Re-Identification
Jinlin Wu,Hao Liu,Yang Yang,Zhen Lei,Shengcai Liao,Stan Z. Li +5 more
- pp 8321-8330
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TLDR
Extensive experiments and ablation studies on seven re-id datasets demonstrate the superiority of the proposed UGA over most state-of-the-art unsupervised and domain adaptation re-ID methods.Abstract:
In this paper, we propose an unsupervised graph association (UGA) framework to learn the underlying viewinvariant representations from the video pedestrian tracklets. The core points of UGA are mining the underlying cross-view associations and reducing the damage of noise associations. To this end, UGA is adopts a two-stage training strategy: (1) intra-camera learning stage and (2) intercamera learning stage. The former learns the intra-camera representation for each camera. While the latter builds a cross-view graph (CVG) to associate different cameras. By doing this, we can learn view-invariant representation for all person. Extensive experiments and ablation studies on seven re-id datasets demonstrate the superiority of the proposed UGA over most state-of-the-art unsupervised and domain adaptation re-id methods.read more
Citations
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Posted Content
Deep Learning for Person Re-identification: A Survey and Outlook
TL;DR: A powerful AGW baseline is designed, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks, and a new evaluation metric (mINP) is introduced, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re- ID system for real applications.
Journal ArticleDOI
Deep Learning for Person Re-Identification: A Survey and Outlook
TL;DR: Zhang et al. as discussed by the authors conducted a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization.
Proceedings ArticleDOI
Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer
TL;DR: Wang et al. as mentioned in this paper proposed a cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modal-specific characteristics to boost the reID performance.
Proceedings ArticleDOI
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning
TL;DR: This work proposes two techniques to improve the discriminative feature learning for MOT by introducing a novel feature interaction mechanism by introducing the Graph Neural Network and proposes a novel joint feature extractor to learn appearance and motion features from 2D and 3D space simultaneously.
Book ChapterDOI
Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lifting
Shengcai Liao,Ling Shao +1 more
TL;DR: Liao et al. as mentioned in this paper formulated person image matching as finding local correspondences in feature maps, and constructed query-adaptive convolution kernels on the fly to achieve local matching.
References
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Proceedings Article
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