Alexander Kolesnikov | DeepAI

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∙ 05/22/2023

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

Scaling laws have been recently employed to derive compute-optimal model...

0 Ibrahim Alabdulmohsin, et al. ∙

∙ 04/08/2023

Capturing dynamical correlations using implicit neural representations

The observation and description of collective excitations in solids is a...

0 Sathya Chitturi, et al. ∙

∙ 03/30/2023

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

There has been a recent explosion of computer vision models which perfor...

4 Lucas Beyer, et al. ∙

∙ 03/27/2023

Sigmoid Loss for Language Image Pre-Training

We propose a simple pairwise sigmoid loss for image-text pre-training. U...

0 Xiaohua Zhai, et al. ∙

∙ 02/16/2023

Tuning computer vision models with task rewards

Misalignment between model predictions and intended usage can be detrime...

0 Andre Susano Pinto, et al. ∙

∙ 12/15/2022

FlexiViT: One Model for All Patch Sizes

Vision Transformers convert images to sequences by slicing them into pat...

15 Lucas Beyer, et al. ∙

∙ 09/14/2022

PaLI: A Jointly-Scaled Multilingual Language-Image Model

Effective scaling and a flexible task interface enable large language mo...

6 Xi Chen, et al. ∙

∙ 05/20/2022

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

We introduce UViM, a unified approach capable of modeling a wide range o...

16 Alexander Kolesnikov, et al. ∙

∙ 05/03/2022

Better plain ViT baselines for ImageNet-1k

It is commonly accepted that the Vision Transformer model requires sophi...

0 Lucas Beyer, et al. ∙

∙ 03/14/2022

Beckmann's approach to multi-item multi-bidder auctions

We consider the problem of revenue-maximizing Bayesian auction design wi...

0 Alexander Kolesnikov, et al. ∙

∙ 11/15/2021

LiT: Zero-Shot Transfer with Locked-image Text Tuning

This paper presents contrastive-tuning, a simple method employing contra...

0 Xiaohua Zhai, et al. ∙

∙ 06/18/2021

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

Vision Transformers (ViT) have been shown to attain highly competitive p...

0 Andreas Steiner, et al. ∙

∙ 06/09/2021

Knowledge distillation: A good teacher is patient and consistent

There is a growing discrepancy in computer vision between large-scale mo...

9 Lucas Beyer, et al. ∙

∙ 06/08/2021

Scaling Vision Transformers

Attention-based neural networks such as the Vision Transformer (ViT) hav...

9 Xiaohua Zhai, et al. ∙

∙ 05/04/2021

MLP-Mixer: An all-MLP Architecture for Vision

Convolutional Neural Networks (CNNs) are the go-to model for computer vi...

18 Ilya Tolstikhin, et al. ∙

∙ 04/09/2021

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

Before deploying machine learning models it is critical to assess their ...

10 Jessica Yung, et al. ∙

∙ 10/22/2020

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

While the Transformer architecture has become the de-facto standard for ...

6 Alexey Dosovitskiy, et al. ∙

∙ 07/16/2020

On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not g...

15 Josip Djolonga, et al. ∙

∙ 06/12/2020

Are we done with ImageNet?

Yes, and no. We ask whether recent progress on the ImageNet classificati...

11 Lucas Beyer, et al. ∙

∙ 12/24/2019

Large Scale Learning of General Visual Representations for Transfer

Transfer of pre-trained representations improves sample efficiency and s...

10 Alexander Kolesnikov, et al. ∙

∙ 05/09/2019

S^4L: Self-Supervised Semi-Supervised Learning

This work tackles the problem of semi-supervised learning of image class...

1 Xiaohua Zhai, et al. ∙

∙ 01/25/2019

Revisiting Self-Supervised Visual Representation Learning

Unsupervised visual representation learning remains a largely unsolved p...

1 Alexander Kolesnikov, et al. ∙

∙ 07/05/2018

Detecting Visual Relationships Using Box Attention

In this paper we propose a new model for detecting visual relationships....

0 Alexander Kolesnikov, et al. ∙

∙ 05/11/2017

Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural im...

0 Amélie Royer, et al. ∙

∙ 12/24/2016

PixelCNN Models with Auxiliary Variables for Natural Image Modeling

We study probabilistic models of natural images and extend the autoregre...

0 Alexander Kolesnikov, et al. ∙

∙ 11/23/2016

iCaRL: Incremental Classifier and Representation Learning

A major open problem on the road to artificial intelligence is the devel...

0 Sylvestre-Alvise Rebuffi, et al. ∙

∙ 03/19/2016

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

We introduce a new loss function for the weakly-supervised training of s...

0 Alexander Kolesnikov, et al. ∙

∙ 04/28/2015

Identifying Reliable Annotations for Large Scale Image Segmentation

Challenging computer vision tasks, in particular semantic image segmenta...

0 Alexander Kolesnikov, et al. ∙

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