Alexander Kolesnikov

According to our database, Alexander Kolesnikov authored at least 43 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

PaliGemma: A versatile 3B VLM for transfer.

Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey A. Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bosnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier J. Hénaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, Xiaohua Zhai

CoRR, 2024

Toward a Diffusion-Based Generalist for Dense Vision Tasks.

Yue Fan, Yongqin Xian, Xiaohua Zhai, Alexander Kolesnikov, Muhammad Ferjad Naeem, Bernt Schiele, Federico Tombari

CoRR, 2024

On Scaling Up a Multilingual Vision and Language Model.

Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

PaLI-3 Vision Language Models: Smaller, Faster, Stronger.

Xi Chen, Xiao Wang, Lucas Beyer, Alexander Kolesnikov, Jialin Wu, Paul Voigtlaender, Basil Mustafa, Sebastian Goodman, Ibrahim Alabdulmohsin, Piotr Padlewski, Daniel Salz, Xi Xiong, Daniel Vlasic, Filip Pavetic, Keran Rong, Tianli Yu, Daniel Keysers, Xiaohua Zhai, Radu Soricut

CoRR, 2023

PaLI-X: On Scaling up a Multilingual Vision and Language Model.

Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut

CoRR, 2023

A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision.

Lucas Beyer, Bo Wan, Gagan Madan, Filip Pavetic, Andreas Steiner, Alexander Kolesnikov, André Susano Pinto, Emanuele Bugliarello, Xiao Wang, Qihang Yu, Liang-Chieh Chen, Xiaohua Zhai

CoRR, 2023

Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design.

Ibrahim M. Alabdulmohsin, Xiaohua Zhai, Alexander Kolesnikov, Lucas Beyer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Tuning Computer Vision Models With Task Rewards.

André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai

Proceedings of the International Conference on Machine Learning, 2023

Scaling Vision Transformers to 22 Billion Parameters.

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby

Proceedings of the International Conference on Machine Learning, 2023

PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V. Thapliyal, James Bradbury, Weicheng Kuo

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Sigmoid Loss for Language Image Pre-Training.

Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

FlexiViT: One Model for All Patch Sizes.

Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, Lucas Beyer

Trans. Mach. Learn. Res., 2022

PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V. Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme, Andreas Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut

CoRR, 2022

Better plain ViT baselines for ImageNet-1k.

Lucas Beyer, Xiaohua Zhai, Alexander Kolesnikov

CoRR, 2022

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes.

Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, Neil Houlsby

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

LiT: Zero-Shot Transfer with Locked-image text Tuning.

Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, Lucas Beyer

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Scaling Vision Transformers.

Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Knowledge distillation: A good teacher is patient and consistent.

Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, Alexander Kolesnikov

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size.

Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai

CoRR, 2021

MLP-Mixer: An all-MLP Architecture for Vision.

Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby

Proceedings of the 9th International Conference on Learning Representations, 2021

On Robustness and Transferability of Convolutional Neural Networks.

Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

The Open Images Dataset V4.

Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper R. R. Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari

Int. J. Comput. Vis., 2020

Are we done with ImageNet?

Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, Aäron van den Oord

CoRR, 2020

Big Transfer (BiT): General Visual Representation Learning.

Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Large Scale Learning of General Visual Representations for Transfer.

Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby

CoRR, 2019

The Visual Task Adaptation Benchmark.

Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby

CoRR, 2019

S⁴L: Self-Supervised Semi-Supervised Learning.

Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer

CoRR, 2019

Detecting Visual Relationships Using Box Attention.

Alexander Kolesnikov, Alina Kuznetsova, Christoph Lampert, Vittorio Ferrari

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

S4L: Self-Supervised Semi-Supervised Learning.

Lucas Beyer, Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Revisiting Self-Supervised Visual Representation Learning.

Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Detecting Visual Relationships Using Box Attention.

Alexander Kolesnikov, Christoph H. Lampert, Vittorio Ferrari

CoRR, 2018

2017

PixelCNN Models with Auxiliary Variables for Natural Image Modeling.

Alexander Kolesnikov, Christoph H. Lampert

Proceedings of the 34th International Conference on Machine Learning, 2017

iCaRL: Incremental Classifier and Representation Learning.

Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, Christoph H. Lampert

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Probabilistic Image Colorization.

Amelie Royer, Alexander Kolesnikov, Christoph H. Lampert

Proceedings of the British Machine Vision Conference 2017, 2017

2016

iCaRL: Incremental Classifier and Representation Learning.

Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Christoph H. Lampert

CoRR, 2016

Deep Probabilistic Modeling of Natural Images using a Pyramid Decomposition.

Alexander Kolesnikov, Christoph H. Lampert

CoRR, 2016

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation.

Alexander Kolesnikov, Christoph H. Lampert

Proceedings of the Computer Vision - ECCV 2016, 2016

Improving Weakly-Supervised Object Localization By Micro-Annotation.

Alexander Kolesnikov, Christoph H. Lampert

Proceedings of the British Machine Vision Conference 2016, 2016

2015

Identifying Reliable Annotations for Large Scale Image Segmentation.

Alexander Kolesnikov, Christoph H. Lampert

CoRR, 2015

2014

Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation.

Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari, Christoph H. Lampert

CoRR, 2014

Closed-Form Approximate CRF Training for Scalable Image Segmentation.

Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari, Christoph H. Lampert

Proceedings of the Computer Vision - ECCV 2014, 2014