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Center-push loss for joint view-based 3D model classification and retrieval feature learning

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Abstract

View-based 3D model classification and retrieval are increasingly important in various fields. High classification accuracy and retrieval precision are urgently needed in the related applications. However, these two topics are always considered separately and very few works give an in-depth analysis of their relations. We would like to argue that although the classification and retrieval focus on different characteristics of embedding features, they are compatible rather than opposed to each other. Inspired by the recent deep metric learning approaches in this field, we propose a novelty loss named center-push loss for joint feature learning. The proposed loss can drive the convolutional neural network to learn object features that distributed compact in intra-class while separated in inter-class effectively. It avoids the annoying triplet sampling operation which always needs a delicately designed sampling strategy for efficient network optimization. The new loss is simple in structure and fast in training and achieves the best performance in classification and retrieval compared with many states of the arts.

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  1. https://github.com/suhangpro/mvcnn/tree/master/utils.

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Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant Nos. U1711265 and 61772158) and Self-Planned Task of State Key Laboratory of Robotics and System (Grant No. SKLRS202014B).

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Dong Wang, Bin Wang, Hongxun Yao & Hong Liu

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  1. Dong Wang
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  2. Bin Wang
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  3. Hongxun Yao
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  4. Hong Liu
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Correspondence to Bin Wang.

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Wang, D., Wang, B., Yao, H. et al. Center-push loss for joint view-based 3D model classification and retrieval feature learning. SIViP 17, 873–880 (2023). https://doi.org/10.1007/s11760-021-01923-4

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  • DOI: https://doi.org/10.1007/s11760-021-01923-4

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