b'Lucas Beyer'

research

∙ 05/26/2023

Three Towers: Flexible Contrastive Learning with Pretrained Image Models

We introduce Three Towers (3T), a flexible method to improve the contras...

0 Jannik Kossen, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 11/17/2022

VeLO: Training Versatile Learned Optimizers by Scaling Up

While deep learning models have replaced hand-designed features across m...

0 Luke Metz, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/17/2021

A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation

This work presents a simple vision transformer design as a strong baseli...

0 Wuyang Chen, et al. ∙

research

∙ 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. ∙

research

∙ 10/25/2021

The Efficiency Misnomer

Model efficiency is a critical aspect of developing and deploying machin...

5 Mostafa Dehghani, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 06/08/2021

Scaling Vision Transformers

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

9 Xiaohua Zhai, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 07/01/2019

MULEX: Disentangling Exploitation from Exploration in Deep RL

An agent learning through interactions should balance its action selecti...

6 Lucas Beyer, et al. ∙

research

∙ 05/30/2019

Deep multi-class learning from label proportions

We propose a learning algorithm capable of learning from label proportio...

0 Gabriel Dulac-Arnold, et al. ∙

research

∙ 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. ∙

research

∙ 01/25/2019

Revisiting Self-Supervised Visual Representation Learning

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

1 Alexander Kolesnikov, et al. ∙

research

∙ 04/26/2018

Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline

In the past decade many robots were deployed in the wild, and people det...

0 Stefan Breuers, et al. ∙

research

∙ 04/06/2018

Deep Person Detection in 2D Range Data

Detecting humans is a key skill for mobile robots and intelligent vehicl...

0 Lucas Beyer, et al. ∙

research

∙ 05/31/2017

The Atari Grand Challenge Dataset

Recent progress in Reinforcement Learning (RL), fueled by its combinatio...

0 Vitaly Kurin, et al. ∙

research

∙ 05/12/2017

Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters

With the rise of end-to-end learning through deep learning, person detec...

0 Lucas Beyer, et al. ∙

research

∙ 03/22/2017

In Defense of the Triplet Loss for Person Re-Identification

In the past few years, the field of computer vision has gone through a r...

1 Alexander Hermans, et al. ∙

research

∙ 03/08/2016

DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data

We introduce the DROW detector, a deep learning based detector for 2D ra...

0 Lucas Beyer, et al. ∙

Lucas Beyer

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