Search Results for author:
Found 33 papers, 21 papers with code
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design
Scaling laws have been recently employed to derive compute-optimal model size (number of parameters) for a given compute duration.
Capturing dynamical correlations using implicit neural representations
The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system.
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision
A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well.
Sigmoid Loss for Language Image Pre-Training
We propose a simple pairwise sigmoid loss for image-text pre-training.
Tuning computer vision models with task rewards
Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Linear-Probe Classification
on ImageNet
(using extra training data)
FlexiViT: One Model for All Patch Sizes
Vision Transformers convert images to sequences by slicing them into patches.
PaLI: A Jointly-Scaled Multilingual Language-Image Model
PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages.
Ranked #1 on
Image Captioning
on nocaps out-of-domain
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks.
Better plain ViT baselines for ImageNet-1k
It is commonly accepted that the Vision Transformer model requires sophisticated regularization techniques to excel at ImageNet-1k scale data.
LiT: Zero-Shot Transfer with Locked-image text Tuning
This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
Knowledge distillation: A good teacher is patient and consistent
In particular, we uncover that there are certain implicit design choices, which may drastically affect the effectiveness of distillation.
Ranked #405 on
Image Classification
on ImageNet
Scaling Vision Transformers
As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90. 45% top-1 accuracy.
Ranked #3 on
Image Classification
on VTAB-1k
(using extra training data)
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vision.
Ranked #18 on
Image Classification
on OmniBenchmark
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 robustness.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts.
Big Transfer (BiT): General Visual Representation Learning
We conduct detailed analysis of the main components that lead to high transfer performance.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
(using extra training data)
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
And, how close are we to general visual representations?
Ranked #9 on
Image Classification
on VTAB-1k
(using extra training data)
The Visual Task Adaptation Benchmark
Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.
S4L: Self-Supervised Semi-Supervised Learning
This work tackles the problem of semi-supervised learning of image classifiers.
Ranked #12 on
Semi-Supervised Image Classification
on ImageNet - 10% labeled data
(Top 5 Accuracy metric)
Revisiting Self-Supervised Visual Representation Learning
Unsupervised visual representation learning remains a largely unsolved problem in computer vision research.
Ranked #106 on
Self-Supervised Image Classification
on ImageNet
Representation Learning
Self-Supervised Image Classification
+1
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
We present Open Images V4, a dataset of 9. 2M images with unified annotations for image classification, object detection and visual relationship detection.
Detecting Visual Relationships Using Box Attention
We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table".
Probabilistic Image Colorization
We develop a probabilistic technique for colorizing grayscale natural images.
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables.
Ranked #13 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
iCaRL: Incremental Classifier and Representation Learning
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data.
Ranked #2 on
Incremental Learning
on ImageNet100 - 10 steps
(# M Params metric)
Improving Weakly-Supervised Object Localization By Micro-Annotation
Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e. g. trains on tracks.
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries.
Identifying Reliable Annotations for Large Scale Image Segmentation
In this work, we present a Gaussian process (GP) based technique for simultaneously identifying which images of a training set have unreliable annotation and learning a segmentation model in which the negative effect of these images is suppressed.
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
It is inspired by existing closed-form expressions for the maximum likelihood parameters of a generative graphical model with tree topology.
