| Delta-encoder: an effective sample synthesis method for few-shot object recognition E Schwartz, L Karlinsky, J Shtok, S Harary, M Marder, A Kumar, R Feris, ... Advances in neural information processing systems 31, 2018 | 240 | 2018 |
| Repmet: Representative-based metric learning for classification and few-shot object detection L Karlinsky, J Shtok, S Harary, E Schwartz, A Aides, R Feris, R Giryes, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 238 | 2019 |
| A broader study of cross-domain few-shot learning Y Guo, NC Codella, L Karlinsky, JV Codella, JR Smith, K Saenko, ... ECCV, 2020 | 144* | 2020 |
| Co-regularized alignment for unsupervised domain adaptation A Kumar, P Sattigeri, K Wadhawan, L Karlinsky, R Feris, B Freeman, ... Advances in Neural Information Processing Systems 31, 2018 | 125 | 2018 |
| A region based convolutional network for tumor detection and classification in breast mammography A Akselrod-Ballin, L Karlinsky, S Alpert, S Hasoul, R Ben-Ari, E Barkan Deep learning and data labeling for medical applications, 197-205, 2016 | 124 | 2016 |
| The chains model for detecting parts by their context L Karlinsky, M Dinerstein, D Harari, S Ullman 2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010 | 95 | 2010 |
| Laso: Label-set operations networks for multi-label few-shot learning A Alfassy, L Karlinsky, A Aides, J Shtok, S Harary, R Feris, R Giryes, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 74 | 2019 |
| Ar-net: Adaptive frame resolution for efficient action recognition Y Meng, CC Lin, R Panda, P Sattigeri, L Karlinsky, A Oliva, K Saenko, ... European Conference on Computer Vision, 86-104, 2020 | 65 | 2020 |
| Using linking features in learning non-parametric part models L Karlinsky, S Ullman European Conference on Computer Vision, 326-339, 2012 | 60 | 2012 |
| Baby steps towards few-shot learning with multiple semantics E Schwartz, L Karlinsky, R Feris, R Giryes, A Bronstein Pattern Recognition Letters 160, 142-147, 2022 | 53 | 2022 |
| Fine-grained recognition of thousands of object categories with single-example training L Karlinsky, J Shtok, Y Tzur, A Tzadok Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 53 | 2017 |
| TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification M Lichtenstein, P Sattigeri, R Feris, R Giryes, L Karlinsky ECCV, 2020 | 47 | 2020 |
| Deep learning for automatic detection of abnormal findings in breast mammography A Akselrod-Ballin, L Karlinsky, A Hazan, R Bakalo, AB Horesh, Y Shoshan, ... Deep learning in medical image analysis and multimodal learning for clinical …, 2017 | 41 | 2017 |
| Domain specific convolutional neural nets for detection of architectural distortion in mammograms R Ben-Ari, A Akselrod-Ballin, L Karlinsky, S Hashoul 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 32 | 2017 |
| Adafuse: Adaptive temporal fusion network for efficient action recognition Y Meng, R Panda, CC Lin, P Sattigeri, L Karlinsky, K Saenko, A Oliva, ... arXiv preprint arXiv:2102.05775, 2021 | 25 | 2021 |
| A CNN based method for automatic mass detection and classification in mammograms A Akselrod-Ballin, L Karlinsky, S Alpert, S Hashoul, R Ben-Ari, E Barkan Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2019 | 25 | 2019 |
| A broad study on the transferability of visual representations with contrastive learning A Islam, CFR Chen, R Panda, L Karlinsky, R Radke, R Feris Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 23 | 2021 |
| Unsupervised classification and part localization by consistency amplification L Karlinsky, M Dinerstein, D Levi, S Ullman European conference on computer vision, 321-335, 2008 | 23 | 2008 |
| Fine-grained angular contrastive learning with coarse labels G Bukchin, E Schwartz, K Saenko, O Shahar, R Feris, R Giryes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 19* | 2021 |
| System and method for detection and classification of findings in images A Akselrod-Ballin, L Karlinsky US Patent 10,223,610, 2019 | 19 | 2019 |
Rogerio FerisResearch Manager, MIT-IBM Watson AI LabVerified email at us.ibm.com
