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Leonid Karlinsky
Leonid Karlinsky
Principal Research Scientist, MIT-IBM Watson AI Lab, IBM Research
Verified email at ibm.com - Homepage
Title
Cited by
Cited by
Year
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
4032018
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
392*2019
A broader study of cross-domain few-shot learning
Y Guo, NC Codella, L Karlinsky, JV Codella, JR Smith, K Saenko, ...
ECCV, 2020
324*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
1842018
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
1662016
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
1352020
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
1302019
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
1002022
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
982010
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
802021
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
M Lichtenstein, P Sattigeri, R Feris, R Giryes, L Karlinsky
ECCV, 2020
802020
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
712017
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
642021
Using linking features in learning non-parametric part models
L Karlinsky, S Ullman
Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012
632012
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
602021
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
562017
Contrastive audio-visual masked autoencoder
Y Gong, A Rouditchenko, AH Liu, D Harwath, L Karlinsky, H Kuehne, ...
arXiv preprint arXiv:2210.07839, 2022
542022
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
542020
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
522021
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
452023
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