DIABLO: Dictionary-based Attention Block for Deep Metric Learning
@article{Jacob2020DIABLODA, title={DIABLO: Dictionary-based Attention Block for Deep Metric Learning}, author={P. Jacob and D. Picard and A. Histace and E. Klein}, journal={Pattern Recognit. Lett.}, year={2020}, volume={135}, pages={99-105} }
Abstract Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image… CONTINUE READING
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References
SHOWING 1-10 OF 28 REFERENCES
Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings
- Computer Science
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- 2019
- 17
- PDF
Deep Metric Learning via Lifted Structured Feature Embedding
- Computer Science
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
- 500
- PDF
BIER — Boosting Independent Embeddings Robustly
- Computer Science
- 2017 IEEE International Conference on Computer Vision (ICCV)
- 2017
- 74
- PDF
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
- Computer Science, Medicine
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- 2020
- 68
- PDF
Learning Deep Features for Discriminative Localization
- Computer Science
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
- 2,964
- PDF
Hardness-Aware Deep Metric Learning
- Computer Science
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
- 49
- PDF
Hard-Aware Deeply Cascaded Embedding
- Computer Science
- 2017 IEEE International Conference on Computer Vision (ICCV)
- 2017
- 168
- PDF