HSE: Hybrid Species Embedding for Deep Metric Learning

Yang, Bailin; Sun, Haoqiang; Li, Frederick W. B.; Chen, Zheng; Cai, Jianlu; Song, Chao

Abstract

Deep metric learning is crucial for finding an embedding function that can generalize to training and testing data, including unknown test classes. However, limited training samples restrict the model's generalization to downstream tasks. While adding new training samples is a promising solution, determining their labels remains a significant challenge. Here, we introduce Hybrid Species Embedding (HSE), which employs mixed sample data augmentations to generate hybrid species and provide additional training signals. We demonstrate that HSE outperforms multiple state-of-the-art methods in improving the metric Recall@K on the CUB-200 , CAR-196 and SOP datasets, thus offering a novel solution to deep metric learning's limitations.

Citation

Yang, B., Sun, H., Li, F. W. B., Chen, Z., Cai, J., & Song, C. (2023, October). HSE: Hybrid Species Embedding for Deep Metric Learning. Presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
• Start Date: Oct 2, 2023
• End Date: Oct 6, 2023
• Acceptance Date: Aug 11, 2023
• Online Publication Date: Jan 15, 2024
• Publication Date: Jan 15, 2024
• Deposit Date: Sep 12, 2023
• Publicly Available Date: Jan 15, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 1550-5499
• Book Title: 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
• ISBN: 9798350307191
• DOI: https://doi.org/10.1109/ICCV51070.2023.01014
• Public URL: https://durham-repository.worktribe.com/output/1735635