{"author":[{"last_name":"Karlinsky","first_name":"Leonid","full_name":"Karlinsky, Leonid"},{"last_name":"Shtok","first_name":"Joseph","full_name":"Shtok, Joseph"},{"first_name":"Sivan","full_name":"Harary, Sivan","last_name":"Harary"},{"full_name":"Schwartz, Eli","first_name":"Eli","last_name":"Schwartz"},{"first_name":"Amit","full_name":"Aides, Amit","last_name":"Aides"},{"full_name":"Feris, Rogerio","first_name":"Rogerio","last_name":"Feris"},{"last_name":"Giryes","first_name":"Raja","full_name":"Giryes, Raja"},{"full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"}],"year":"2020","publication_identifier":{"eissn":["2575-7075"],"isbn":["9781728132945"]},"doi":"10.1109/cvpr.2019.00534","extern":"1","article_number":"8953439","type":"conference","date_created":"2024-10-08T13:08:09Z","month":"01","date_updated":"2024-12-05T15:38:16Z","scopus_import":"1","status":"public","_id":"18258","day":"09","citation":{"chicago":"Karlinsky, Leonid, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, and Alex M. Bronstein. “Repmet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection.” In <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/cvpr.2019.00534\">https://doi.org/10.1109/cvpr.2019.00534</a>.","apa":"Karlinsky, L., Shtok, J., Harary, S., Schwartz, E., Aides, A., Feris, R., … Bronstein, A. M. (2020). Repmet: Representative-based metric learning for classification and few-shot object detection. In <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>. Long Beach, CA, United States: IEEE. <a href=\"https://doi.org/10.1109/cvpr.2019.00534\">https://doi.org/10.1109/cvpr.2019.00534</a>","mla":"Karlinsky, Leonid, et al. “Repmet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection.” <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>, 8953439, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/cvpr.2019.00534\">10.1109/cvpr.2019.00534</a>.","ieee":"L. Karlinsky <i>et al.</i>, “Repmet: Representative-based metric learning for classification and few-shot object detection,” in <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>, Long Beach, CA, United States, 2020.","ama":"Karlinsky L, Shtok J, Harary S, et al. Repmet: Representative-based metric learning for classification and few-shot object detection. In: <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/cvpr.2019.00534\">10.1109/cvpr.2019.00534</a>","short":"L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, A.M. Bronstein, in:, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020.","ista":"Karlinsky L, Shtok J, Harary S, Schwartz E, Aides A, Feris R, Giryes R, Bronstein AM. 2020. Repmet: Representative-based metric learning for classification and few-shot object detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8953439."},"article_processing_charge":"No","quality_controlled":"1","abstract":[{"text":"Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.","lang":"eng"}],"oa_version":"None","date_published":"2020-01-09T00:00:00Z","title":"Repmet: Representative-based metric learning for classification and few-shot object detection","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publisher":"IEEE","publication_status":"published","language":[{"iso":"eng"}],"publication":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","conference":{"location":"Long Beach, CA, United States","name":"32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition","end_date":"2019-06-20","start_date":"2019-06-15"}}