• Corpus ID: 30887844

Hierarchical Cross Network for Person Re-identification

  title={Hierarchical Cross Network for Person Re-identification},
  author={Huan-Cheng Hsu and Ching-Hang Chen and Hsiao-Rong Tyan and Hong-Yuan Mark Liao},
Person re-identification (person re-ID) aims at matching target person(s) grabbed from different and non-overlapping camera views. [...] Key Method In addition to the backbone model of a conventional CNN, HCN is equipped with two additional maps called hierarchical cross feature maps. The maps of an HCN are formed by merging layers with different resolutions and semantic levels.Expand
2 Citations
Person re-identification based on multi-level feature complementarity of cross-attention with part metric learning
A novel person re-identification scheme that achieves high values on Rank-k and mAP on Market-1501, Duke-MTMC and CUHK03-NP and can reduce both within-class and increase between-class distance of the person parts.
Random Occlusion-recovery for Person Re-identification
A generative adversarial network (GAN) model is proposed to use paired occlusion and original images to synthesize the de-occluded images that similar but not identical to the original images.


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Person Re-identification: Past, Present and Future
The history of person re-identification and its relationship with image classification and instance retrieval is introduced and two new re-ID tasks which are much closer to real-world applications are described and discussed.
End-to-End Comparative Attention Networks for Person Re-Identification
This paper proposes a new soft attention-based model, i.e., the end-to-end comparative attention network (CAN), specifically tailored for the task of person re-identification that outperforms well established baselines significantly and offers the new state-of-the-art performance.
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
A novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter is proposed and significantly outperforms state-of-the-art methods on this dataset.
Gated Siamese Convolutional Neural Network Architecture for Human Re-identification
A gating function is proposed to selectively emphasize such fine common local patterns that may be essential to distinguish positive pairs from hard negative pairs by comparing the mid-level features across pairs of images.
Similarity Learning with Spatial Constraints for Person Re-identification
This work learns a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion, and takes advantage of the recently proposed polynomial feature map to describe the matching within each subregion and inject all the feature maps into a unified framework.
Person re-identification by Local Maximal Occurrence representation and metric learning
This paper proposes an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA), and presents a practical computation method for XQDA.
Learning a Discriminative Null Space for Person Re-identification
  • Li Zhang, T. Xiang, S. Gong
  • Computer Science, Mathematics
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
This work proposes to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data, which has a fixed dimension, a closed-form solution and is very efficient to compute.
Person re-identification by symmetry-driven accumulation of local features
In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the
Person Reidentification Using Spatiotemporal Appearance
A novel spatiotemporal segmentation algorithm is employed to generate salient edgels that are robust to changes in appearance of clothing and invariant signatures are generated by combining normalized color and salient edgel histograms.