| 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 | 464 | 2019 |
| 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 | 459 | 2018 |
| A broader study of cross-domain few-shot learning Y Guo, NC Codella, L Karlinsky, JV Codella, JR Smith, K Saenko, ... ECCV, 2020 | 420* | 2020 |
| CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning JS Smith, L Karlinsky, V Gutta, P Cascante-Bonilla, D Kim, A Arbelle, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 254* | 2023 |
| 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 | 203 | 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: First …, 2016 | 189 | 2016 |
| Ar-net: Adaptive frame resolution for efficient action recognition Y Meng, CC Lin, R Panda, P Sattigeri, L Karlinsky, A Oliva, K Saenko, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 178 | 2020 |
| 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 | 151 | 2019 |
| Listen, think, and understand Y Gong, H Luo, AH Liu, L Karlinsky, J Glass arXiv preprint arXiv:2305.10790, 2023 | 141 | 2023 |
| Contrastive audio-visual masked autoencoder Y Gong, A Rouditchenko, AH Liu, D Harwath, L Karlinsky, H Kuehne, ... arXiv preprint arXiv:2210.07839, 2022 | 140 | 2022 |
| 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 | 121 | 2022 |
| 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 | 115 | 2021 |
| Multitask prompt tuning enables parameter-efficient transfer learning Z Wang, R Panda, L Karlinsky, R Feris, H Sun, Y Kim arXiv preprint arXiv:2303.02861, 2023 | 108 | 2023 |
| 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 | 104 | 2010 |
| TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification M Lichtenstein, P Sattigeri, R Feris, R Giryes, L Karlinsky ECCV, 2020 | 91 | 2020 |
| Dynamic distillation network for cross-domain few-shot recognition with unlabeled data A Islam, CFR Chen, R Panda, L Karlinsky, R Feris, RJ Radke Advances in Neural Information Processing Systems 34, 3584-3595, 2021 | 90 | 2021 |
| 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 | 86 | 2017 |
| OnlineAugment: Online data augmentation with less domain knowledge Z Tang, Y Gao, L Karlinsky, P Sattigeri, R Feris, D Metaxas Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 79 | 2020 |
| Whisper-at: Noise-robust automatic speech recognizers are also strong general audio event taggers Y Gong, S Khurana, L Karlinsky, J Glass arXiv preprint arXiv:2307.03183, 2023 | 71 | 2023 |
| 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 | 71 | 2021 |
Rogerio FerisResearch Manager, MIT-IBM Watson AI LabVerified email at us.ibm.com
