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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...
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04/08/2023
Capturing dynamical correlations using implicit neural representations
The observation and description of collective excitations in solids is a...
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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...
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03/27/2023
Sigmoid Loss for Language Image Pre-Training
We propose a simple pairwise sigmoid loss for image-text pre-training. U...
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02/16/2023
Tuning computer vision models with task rewards
Misalignment between model predictions and intended usage can be detrime...
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12/15/2022
FlexiViT: One Model for All Patch Sizes
Vision Transformers convert images to sequences by slicing them into pat...
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09/14/2022
PaLI: A Jointly-Scaled Multilingual Language-Image Model
Effective scaling and a flexible task interface enable large language mo...
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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...
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05/03/2022
Better plain ViT baselines for ImageNet-1k
It is commonly accepted that the Vision Transformer model requires sophi...
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03/14/2022
Beckmann's approach to multi-item multi-bidder auctions
We consider the problem of revenue-maximizing Bayesian auction design wi...
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11/15/2021
LiT: Zero-Shot Transfer with Locked-image Text Tuning
This paper presents contrastive-tuning, a simple method employing contra...
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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...
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06/09/2021
Knowledge distillation: A good teacher is patient and consistent
There is a growing discrepancy in computer vision between large-scale mo...
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06/08/2021
Scaling Vision Transformers
Attention-based neural networks such as the Vision Transformer (ViT) hav...
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05/04/2021
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vi...
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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 ...
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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 ...
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07/16/2020
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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06/12/2020
Are we done with ImageNet?
Yes, and no. We ask whether recent progress on the ImageNet classificati...
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12/24/2019
Large Scale Learning of General Visual Representations for Transfer
Transfer of pre-trained representations improves sample efficiency and s...
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05/09/2019
S^4L: Self-Supervised Semi-Supervised Learning
This work tackles the problem of semi-supervised learning of image class...
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01/25/2019
Revisiting Self-Supervised Visual Representation Learning
Unsupervised visual representation learning remains a largely unsolved p...
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07/05/2018
Detecting Visual Relationships Using Box Attention
In this paper we propose a new model for detecting visual relationships....
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05/11/2017
Probabilistic Image Colorization
We develop a probabilistic technique for colorizing grayscale natural im...
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12/24/2016
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
We study probabilistic models of natural images and extend the autoregre...
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11/23/2016
iCaRL: Incremental Classifier and Representation Learning
A major open problem on the road to artificial intelligence is the devel...
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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...
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04/28/2015
