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<!DOCTYPE html>
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    <title>Pirt</title>
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          <h1 class="title">
            Pose-guided Inter- and Intra-part Relational Transformer for
          </h1>
          <h1 class="title">
            Occluded Person Re-Identification
          </h1>

          <div class="author">Zhongxing Ma<sup>1</sup></div>
          <div class="author">Yifan Zhao<sup>1</sup></div>
          <div class="author">Jia Li<sup>1,2</sup></div>
          <div class="group">
            <a href="http://cvteam.net/">CVTEAM</a>
          </div>
          <div class="aff">
            <p><sup>1</sup>State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University, Beijing, China</p>
            <p><sup>2</sup>Peng Cheng Laboratory, Shenzhen, China</p>
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          <div class="con">
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            ACM MM 2021
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              <a href="https://arxiv.org/" target="_blank">
                <p class="link">Paper</p>
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              <a href="https://github.com/iCVTEAM/Pirt/" target="_blank">
                <p class="link">Code</p>
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          The framework of our model.
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          <h1 style="">Abstract</h1>
          <p  style="text-align: justify; font-size: 17px;">
            Person Re-Identification (Re-Id) in occlusion scenarios is a challenging problem because a pedestrian can be partially occluded. The use of local information for feature extraction and matching is still necessary. Therefore, we propose a Pose-guided inter- and intra-part relational transformer (Pirt) for occluded person Re-Id, which builds part-aware long-term correlations by introducing transformer. In our framework, we firstly develop a pose-guided feature extraction module with regional grouping and mask construction for robust feature representations. The positions of a pedestrian in the image under surveillance scenarios are relatively fixed, hence we propose intra-part and inter-part relational transformer. The intra-part module creates local relations with mask-guided features, while the inter-part relationship builds correlations with transformers, to develop cross relationships between part nodes. With the collaborative learning inter- and intra-part relationships, experiments reveal that our proposed Pirt model achieves a new state of the art on the public occluded dataset, and further extensions on standard non-occluded person Re-Id datasets also reveal our comparable performances.
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          <h1>
             Results
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      BibTex Citation
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<pre style="background-color:inherit;padding: 0px;" id="bibtex-info">
 @misc{ma2021poseguided,
      title={Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification},
      author={Zhongxing Ma and Yifan Zhao and Jia Li},
      year={2021},
      eprint={2109.03483},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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