{"publication_identifier":{"eissn":["2575-7075"],"isbn":["9781728132945"]},"author":[{"full_name":"Alfassy, Amit","first_name":"Amit","last_name":"Alfassy"},{"full_name":"Karlinsky, Leonid","first_name":"Leonid","last_name":"Karlinsky"},{"last_name":"Aides","first_name":"Amit","full_name":"Aides, Amit"},{"last_name":"Shtok","first_name":"Joseph","full_name":"Shtok, Joseph"},{"last_name":"Harary","first_name":"Sivan","full_name":"Harary, Sivan"},{"full_name":"Feris, Rogerio","first_name":"Rogerio","last_name":"Feris"},{"last_name":"Giryes","full_name":"Giryes, Raja","first_name":"Raja"},{"full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"}],"arxiv":1,"scopus_import":"1","date_updated":"2024-12-05T15:33:21Z","date_created":"2024-10-08T13:08:26Z","type":"conference","_id":"18259","article_processing_charge":"No","citation":{"short":"A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, A.M. Bronstein, in:, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020.","ista":"Alfassy A, Karlinsky L, Aides A, Shtok J, Harary S, Feris R, Giryes R, Bronstein AM. 2020. Laso: Label-set operations networks for multi-label few-shot learning. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8954088.","ieee":"A. Alfassy <i>et al.</i>, “Laso: Label-set operations networks for multi-label few-shot learning,” in <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>, Long Beach, CA, United States, 2020.","ama":"Alfassy A, Karlinsky L, Aides A, et al. Laso: Label-set operations networks for multi-label few-shot learning. 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.2019.00671\">10.1109/cvpr.2019.00671</a>","mla":"Alfassy, Amit, et al. “Laso: Label-Set Operations Networks for Multi-Label Few-Shot Learning.” <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>, 8954088, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/cvpr.2019.00671\">10.1109/cvpr.2019.00671</a>.","chicago":"Alfassy, Amit, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, and Alex M. Bronstein. “Laso: Label-Set Operations Networks for Multi-Label Few-Shot Learning.” In <i>2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/cvpr.2019.00671\">https://doi.org/10.1109/cvpr.2019.00671</a>.","apa":"Alfassy, A., Karlinsky, L., Aides, A., Shtok, J., Harary, S., Feris, R., … Bronstein, A. M. (2020). Laso: Label-set operations networks for multi-label few-shot learning. 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.2019.00671\">https://doi.org/10.1109/cvpr.2019.00671</a>"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","conference":{"name":"32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition","location":"Long Beach, CA, United States","start_date":"2019-06-15","end_date":"2019-06-20"},"year":"2020","external_id":{"arxiv":["1902.09811"]},"oa":1,"extern":"1","doi":"10.1109/cvpr.2019.00671","month":"01","article_number":"8954088","day":"09","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1902.09811","open_access":"1"}],"status":"public","abstract":[{"text":"Example synthesis is one of the leading methods to tackle the problem of few-shot learning, where only a small number of samples per class are available. However, current synthesis approaches only address the scenario of a single category label per image. In this work, we propose a novel technique for synthesizing samples with multiple labels for the (yet unhandled) multi-label few-shot classification scenario. We propose to combine pairs of given examples in feature space, so that the resulting synthesized feature vectors will correspond to examples whose label sets are obtained through certain set operations on the label sets of the corresponding input pairs. Thus, our method is capable of producing a sample containing the intersection, union or set-difference of labels present in two input samples. As we show, these set operations generalize to labels unseen during training. This enables performing augmentation on examples of novel categories, thus, facilitating multi-label few-shot classifier learning. We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning. We propose a benchmark for this new and challenging task and show that our method compares favorably to all the common baselines.","lang":"eng"}],"quality_controlled":"1","publisher":"IEEE","title":"Laso: Label-set operations networks for multi-label few-shot learning","date_published":"2020-01-09T00:00:00Z","publication":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","language":[{"iso":"eng"}],"publication_status":"published"}