Abstract
End-to-end person search is a novel task that integrates pedestrian detection and person re-identification (re-ID) into a joint optimization framework. However, the pedestrian features learned by most existing methods are not discriminative enough due to the potential adverse interaction between detection and re-ID tasks and the lack of discriminative power of re-ID loss. To this end, we propose an Improved Model Structure (IMS) with a novel re-ID loss function called Cosine Margin Online Instance Matching (CM-OIM) loss. Firstly, we design a model structure more suitable for person search, which alleviates the adverse interaction between the detection and re-ID parts by reasonably decreasing the network layers shared by them. Then, we conduct a full investigation of the weight of re-ID loss, which we argue plays an important role in end-to-end person search models. Finally, we improve the Online Instance Matching (OIM) loss by adopting a more robust online update strategy, and importing a cosine margin into it to increase the intra-class compactness of the features learned. Extensive experiments on two challenging datasets CUHK-SYSU and PRW demonstrate our approach outperforms the state-of-the-arts.
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Acknowledgments
This work has been supported by Science and Technology Commission of Shanghai Municipality (No. 17511106902), Shanghai Youth Science and Technology Development Funds (No. 18QB1403900), Shanghai Artificial intelligence development project (No. 2018-RGZN-02009).
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Chen, H., Zhu, M., Cai, X., Luo, J., Qiu, Y. (2020). Improved Model Structure with Cosine Margin OIM Loss for End-to-End Person Search. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_34
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DOI: https://doi.org/10.1007/978-3-030-37731-1_34
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