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HSE: Hybrid Species Embedding for Deep Metric Learning

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


Bailin Yang

Haoqiang Sun

Zheng Chen

Jianlu Cai

Chao Song


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.


Yang, B., Sun, H., Li, F. W. B., Chen, Z., Cai, J., & Song, C. (in press). HSE: Hybrid Species Embedding for Deep Metric Learning.

Conference Name International Conference on Computer Vision
Conference Location Paris
Start Date Oct 2, 2023
End Date Oct 6, 2023
Acceptance Date Aug 11, 2023
Deposit Date Sep 12, 2023
Publicly Available Date Sep 27, 2023
Public URL