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2020 – today
- 2023
[e8]Leonid Karlinsky, Tomer Michaeli
, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13801, Springer 2023, ISBN 978-3-031-25055-2 [contents]- [e7]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13802, Springer 2023, ISBN 978-3-031-25062-0 [contents] - [e6]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13803, Springer 2023, ISBN 978-3-031-25065-1 [contents] - [e5]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IV. Lecture Notes in Computer Science 13804, Springer 2023, ISBN 978-3-031-25068-2 [contents] - [e4]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part V. Lecture Notes in Computer Science 13805, Springer 2023, ISBN 978-3-031-25071-2 [contents] - [e3]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part VI. Lecture Notes in Computer Science 13806, Springer 2023, ISBN 978-3-031-25074-3 [contents] - [e2]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part VII. Lecture Notes in Computer Science 13807, Springer 2023, ISBN 978-3-031-25081-1 [contents] - [e1]Leonid Karlinsky, Tomer Michaeli, Ko Nishino:
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part VIII. Lecture Notes in Computer Science 13808, Springer 2023, ISBN 978-3-031-25084-2 [contents] - [i36]Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim:
Learning to Grow Pretrained Models for Efficient Transformer Training. CoRR abs/2303.00980 (2023) - [i35]Zhen Wang, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Huan Sun, Yoon Kim:
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning. CoRR abs/2303.02861 (2023) - [i34]Wei Lin, Leonid Karlinsky, Nina Shvetsova, Horst Possegger, Mateusz Kozinski, Rameswar Panda, Rogério Feris, Hilde Kuehne, Horst Bischof:
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge. CoRR abs/2303.08914 (2023) - 2022
- [j4]Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Schmidt Feris:
A Maximal Correlation Framework for Fair Machine Learning. Entropy 24(4): 461 (2022) - [j3]Eli Schwartz, Leonid Karlinsky, Rogério Feris, Raja Giryes, Alexander M. Bronstein:
Baby steps towards few-shot learning with multiple semantics. Pattern Recognit. Lett. 160: 142-147 (2022) - [c28]Sivan Harary, Eli Schwartz, Assaf Arbelle, Peter W. J. Staar, Shady Abu Hussein, Elad Amrani, Roei Herzig, Amit Alfassy, Raja Giryes, Hilde Kuehne, Dina Katabi, Kate Saenko, Rogério Feris, Leonid Karlinsky:
Unsupervised Domain Generalization by Learning a Bridge Across Domains. CVPR 2022: 5270-5280 - [c27]Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu Richard Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogério Schmidt Feris:
Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data. CVPR 2022: 9184-9194 - [c26]Elad Amrani, Leonid Karlinsky, Alexander M. Bronstein:
Self-Supervised Classification Network. ECCV (31) 2022: 116-132 - [c25]Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Feris:
A Maximal Correlation Approach to Imposing Fairness in Machine Learning. ICASSP 2022: 3523-3527 - [i33]Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson:
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens. CoRR abs/2206.06346 (2022) - [i32]Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson:
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022. CoRR abs/2206.07689 (2022) - [i31]Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Kate Saenko, Peter W. J. Staar, Rogério Feris, Leonid Karlinsky:
FETA: Towards Specializing Foundation Models for Expert Task Applications. CoRR abs/2209.03648 (2022) - [i30]Felix Vogel, Nina Shvetsova, Leonid Karlinsky, Hilde Kuehne:
VL-Taboo: An Analysis of Attribute-based Zero-shot Capabilities of Vision-Language Models. CoRR abs/2209.06103 (2022) - [i29]Andrew Rouditchenko, Yung-Sung Chuang, Nina Shvetsova, Samuel Thomas, Rogério Feris, Brian Kingsbury, Leonid Karlinsky, David Harwath, Hilde Kuehne, James R. Glass:
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval. CoRR abs/2210.03625 (2022) - [i28]Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava:
On the Importance of Calibration in Semi-supervised Learning. CoRR abs/2210.04783 (2022) - [i27]Yuan Gong, Andrew Rouditchenko, Alexander H. Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, James R. Glass:
Contrastive Audio-Visual Masked Autoencoder. CoRR abs/2210.07839 (2022) - [i26]James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David D. Cox, Diyi Yang, Zsolt Kira, Rogério Feris, Leonid Karlinsky:
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning. CoRR abs/2211.09790 (2022) - [i25]Sivan Doveh, Assaf Arbelle, Sivan Harary, Rameswar Panda, Roei Herzig, Eli Schwartz, Donghyun Kim, Raja Giryes, Rogério Feris, Shimon Ullman, Leonid Karlinsky:
Teaching Structured Vision&Language Concepts to Vision&Language Models. CoRR abs/2211.11733 (2022) - [i24]Paola Cascante-Bonilla, Leonid Karlinsky, James Seale Smith, Yanjun Qi, Vicente Ordonez:
On the Transferability of Visual Features in Generalized Zero-Shot Learning. CoRR abs/2211.12494 (2022) - [i23]James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogério Feris, Zsolt Kira:
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning. CoRR abs/2211.13218 (2022) - [i22]Eli Schwartz, Assaf Arbelle, Leonid Karlinsky, Sivan Harary, Florian Scheidegger, Sivan Doveh, Raja Giryes:
MAEDAY: MAE for few and zero shot AnomalY-Detection. CoRR abs/2211.14307 (2022) - [i21]Roei Herzig, Ofir Abramovich, Elad Ben-Avraham, Assaf Arbelle, Leonid Karlinsky, Ariel Shamir, Trevor Darrell, Amir Globerson:
PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data. CoRR abs/2212.04821 (2022) - 2021
- [j2]Sivan Doveh, Eli Schwartz, Chao Xue, Rogério Feris, Alexander M. Bronstein, Raja Giryes, Leonid Karlinsky:
MetAdapt: Meta-learned task-adaptive architecture for few-shot classification. Pattern Recognit. Lett. 149: 130-136 (2021) - [c24]Leonid Karlinsky, Joseph Shtok, Amit Alfassy, Moshe Lichtenstein, Sivan Harary, Eli Schwartz, Sivan Doveh, Prasanna Sattigeri, Rogério Feris, Alex M. Bronstein, Raja Giryes:
StarNet: towards Weakly Supervised Few-Shot Object Detection. AAAI 2021: 1743-1753 - [c23]Guy Bukchin, Eli Schwartz, Kate Saenko, Ori Shahar, Rogério Feris, Raja Giryes, Leonid Karlinsky:
Fine-Grained Angular Contrastive Learning With Coarse Labels. CVPR 2021: 8730-8740 - [c22]Assaf Arbelle, Sivan Doveh, Amit Alfassy, Joseph Shtok, Guy Lev, Eli Schwartz, Hilde Kuehne, Hila Barak Levi, Prasanna Sattigeri, Rameswar Panda, Chun-Fu Chen, Alex M. Bronstein, Kate Saenko, Shimon Ullman, Raja Giryes, Rogério Feris, Leonid Karlinsky:
Detector-Free Weakly Supervised Grounding by Separation. ICCV 2021: 1781-1792 - [c21]Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Richard J. Radke, Rogério Feris:
A Broad Study on the Transferability of Visual Representations with Contrastive Learning. ICCV 2021: 8825-8835 - [c20]Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogério Feris:
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition. ICLR 2021 - [c19]Ashraful Islam, Chun-Fu (Richard) Chen, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Richard J. Radke:
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data. NeurIPS 2021: 3584-3595 - [i20]Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogério Feris:
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition. CoRR abs/2102.05775 (2021) - [i19]Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Richard J. Radke, Rogério Feris:
A Broad Study on the Transferability of Visual Representations with Contrastive Learning. CoRR abs/2103.13517 (2021) - [i18]Assaf Arbelle, Sivan Doveh, Amit Alfassy, Joseph Shtok, Guy Lev, Eli Schwartz, Hilde Kuehne, Hila Barak Levi, Prasanna Sattigeri, Rameswar Panda, Chun-Fu Chen, Alex M. Bronstein, Kate Saenko, Shimon Ullman, Raja Giryes, Rogério Feris, Leonid Karlinsky:
Detector-Free Weakly Supervised Grounding by Separation. CoRR abs/2104.09829 (2021) - [i17]Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Richard J. Radke:
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data. CoRR abs/2106.07807 (2021) - [i16]Joseph Shtok, Sivan Harary, Ophir Azulai, Adi Raz Goldfarb, Assaf Arbelle, Leonid Karlinsky:
CHARTER: heatmap-based multi-type chart data extraction. CoRR abs/2111.14103 (2021) - [i15]Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogério Schmidt Feris:
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data. CoRR abs/2112.00054 (2021) - [i14]Sivan Harary, Eli Schwartz, Assaf Arbelle, Peter W. J. Staar, Shady Abu Hussein, Elad Amrani, Roei Herzig, Amit Alfassy, Raja Giryes, Hilde Kuehne, Dina Katabi, Kate Saenko, Rogério Feris, Leonid Karlinsky:
Unsupervised Domain Generalization by Learning a Bridge Across Domains. CoRR abs/2112.02300 (2021) - 2020
- [c18]Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogério Feris:
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition. ECCV (7) 2020: 86-104 - [c17]Yunhui Guo, Noel Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, Rogério Feris:
A Broader Study of Cross-Domain Few-Shot Learning. ECCV (27) 2020: 124-141 - [c16]Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogério Feris, Dimitris N. Metaxas:
OnlineAugment: Online Data Augmentation with Less Domain Knowledge. ECCV (7) 2020: 313-329 - [c15]Moshe Lichtenstein, Prasanna Sattigeri, Rogério Feris, Raja Giryes, Leonid Karlinsky:
TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification. ECCV (7) 2020: 522-539 - [i13]Moshe Lichtenstein, Prasanna Sattigeri, Rogério Schmidt Feris, Raja Giryes, Leonid Karlinsky:
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification. CoRR abs/2003.06670 (2020) - [i12]Leonid Karlinsky, Joseph Shtok, Amit Alfassy, Moshe Lichtenstein, Sivan Harary, Eli Schwartz, Sivan Doveh, Prasanna Sattigeri, Rogério Schmidt Feris, Alexander M. Bronstein, Raja Giryes:
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification. CoRR abs/2003.06798 (2020) - [i11]Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogério Feris, Dimitris N. Metaxas:
OnlineAugment: Online Data Augmentation with Less Domain Knowledge. CoRR abs/2007.09271 (2020) - [i10]Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogério Feris:
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition. CoRR abs/2007.15796 (2020) - [i9]Guy Bukchin, Eli Schwartz, Kate Saenko, Ori Shahar, Rogério Feris, Raja Giryes, Leonid Karlinsky:
Fine-grained Angular Contrastive Learning with Coarse Labels. CoRR abs/2012.03515 (2020) - [i8]Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Feris:
A Maximal Correlation Approach to Imposing Fairness in Machine Learning. CoRR abs/2012.15259 (2020)
2010 – 2019
- 2019
- [j1]Ayelet Akselrod-Ballin, Leonid Karlinsky, Sharon Alpert, Sharbell Y. Hasoul, Rami Ben-Ari, Ella Barkan:
A CNN based method for automatic mass detection and classification in mammograms. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 7(3): 242-249 (2019) - [c14]Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein:
RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection. CVPR 2019: 5197-5206 - [c13]Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein:
LaSO: Label-Set Operations Networks for Multi-Label Few-Shot Learning. CVPR 2019: 6548-6557 - [i7]Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein:
LaSO: Label-Set Operations networks for multi-label few-shot learning. CoRR abs/1902.09811 (2019) - [i6]Eli Schwartz, Leonid Karlinsky, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein:
Baby steps towards few-shot learning with multiple semantics. CoRR abs/1906.01905 (2019) - [i5]Sivan Doveh, Eli Schwartz, Chao Xue, Rogério Schmidt Feris, Alexander M. Bronstein, Raja Giryes, Leonid Karlinsky:
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification. CoRR abs/1912.00412 (2019) - [i4]Yunhui Guo, Noel C. F. Codella, Leonid Karlinsky, John R. Smith, Tajana Rosing, Rogério Schmidt Feris:
A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning. CoRR abs/1912.07200 (2019) - 2018
- [c12]Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein:
Delta-encoder: an effective sample synthesis method for few-shot object recognition. NeurIPS 2018: 2850-2860 - [c11]Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, Bill Freeman, Gregory W. Wornell:
Co-regularized Alignment for Unsupervised Domain Adaptation. NeurIPS 2018: 9367-9378 - [i3]Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Sharathchandra Pankanti, Rogério Schmidt Feris, Abhishek Kumar, Raja Giryes, Alexander M. Bronstein:
RepMet: Representative-based metric learning for classification and one-shot object detection. CoRR abs/1806.04728 (2018) - [i2]Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Rogério Schmidt Feris, Abhishek Kumar, Raja Giryes, Alexander M. Bronstein:
Delta-encoder: an effective sample synthesis method for few-shot object recognition. CoRR abs/1806.04734 (2018) - [i1]Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, William T. Freeman, Gregory W. Wornell:
Co-regularized Alignment for Unsupervised Domain Adaptation. CoRR abs/1811.05443 (2018) - 2017
- [c10]Ethan Hadar, Joseph Shtok, Benjamin Cohen, Yochay Tzur, Leonid Karlinsky:
Hybrid Remote Expert - an Emerging Pattern of Industrial Remote Support. CAiSE-Forum-DC 2017: 33-40 - [c9]Leonid Karlinsky, Joseph Shtok, Yochay Tzur, Asaf Tzadok:
Fine-Grained Recognition of Thousands of Object Categories with Single-Example Training. CVPR 2017: 965-974 - [c8]Rami Ben-Ari, Ayelet Akselrod-Ballin, Leonid Karlinsky, Sharbell Y. Hashoul:
Domain specific convolutional neural nets for detection of architectural distortion in mammograms. ISBI 2017: 552-556 - [c7]Ayelet Akselrod-Ballin, Leonid Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan, Ella Barkan:
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography. DLMIA/ML-CDS@MICCAI 2017: 321-329 - 2016
- [c6]Ayelet Akselrod-Ballin, Leonid Karlinsky, Sharon Alpert, Sharbell Y. Hasoul, Rami Ben-Ari, Ella Barkan:
A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography. LABELS/DLMIA@MICCAI 2016: 197-205 - 2012
- [c5]Leonid Karlinsky, Shimon Ullman:
Using Linking Features in Learning Non-parametric Part Models. ECCV (3) 2012: 326-339 - 2010
- [c4]Leonid Karlinsky, Michael Dinerstein, Daniel Harari, Shimon Ullman:
The chains model for detecting parts by their context. CVPR 2010: 25-32 - [c3]Leonid Karlinsky, Michael Dinerstein, Shimon Ullman:
Using body-anchored priors for identifying actions in single images. NIPS 2010: 1072-1080
2000 – 2009
- 2009
- [c2]Leonid Karlinsky, Michael Dinerstein, Shimon Ullman:
Unsupervised feature optimization (UFO): Simultaneous selection of multiple features with their detection parameters. CVPR 2009: 1263-1270 - 2008
- [c1]Leonid Karlinsky, Michael Dinerstein, Dan Levi, Shimon Ullman:
Unsupervised Classification and Part Localization by Consistency Amplification. ECCV (2) 2008: 321-335
Coauthor Index
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