Search Results for author:
Found 28 papers, 10 papers with code
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged.
PaLI-X: On Scaling up a Multilingual Vision and Language Model
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture.
Ranked #1 on
Fine-Grained Image Recognition
on OVEN
End-to-End Spatio-Temporal Action Localisation with Video Transformers
The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks.
Ranked #1 on
Action Recognition
on AVA v2.1
(using extra training data)
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)
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
In this paper, we propose new differentiable and sparse top-k operators.
Beyond Transfer Learning: Co-finetuning for Action Localisation
Transfer learning is the predominant paradigm for training deep networks on small target datasets.
Revisiting the Calibration of Modern Neural Networks
Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks.
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
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.
Representation learning from videos in-the-wild: An object-centric approach
We propose a method to learn image representations from uncurated videos.
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts.
You Only Train Once: Loss-Conditional Training of Deep Networks
At test time a model trained this way can be conditioned to generate outputs corresponding to any loss from the training distribution of losses.
Fast Differentiable Sorting and Ranking
While numerous works have proposed differentiable proxies to sorting and ranking, they do not achieve the $O(n \log n)$ time complexity one would expect from sorting and ranking operations.
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI).
Ranked #15 on
Image Classification
on VTAB-1k
(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 #10 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.
On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data.
Practical and Consistent Estimation of f-Divergences
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.
Precision-Recall Curves Using Information Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, the development of tools for diagnosing their failures and assessing their performance has advanced at a much slower pace.
Provable Variational Inference for Constrained Log-Submodular Models
Submodular maximization problems appear in several areas of machine learning and data science, as many useful modelling concepts such as diversity and coverage satisfy this natural diminishing returns property.
Differentiable Learning of Submodular Models
In this paper we focus on the problem of submodular minimization, for which we show that such layers are indeed possible.
Learning Implicit Generative Models Using Differentiable Graph Tests
Recently, there has been a growing interest in the problem of learning rich implicit models - those from which we can sample, but can not evaluate their density.
Cooperative Graphical Models
We study a rich family of distributions that capture variable interactions significantly more expressive than those representable with low-treewidth or pairwise graphical models, or log-supermodular models.
Variational Inference in Mixed Probabilistic Submodular Models
We consider the problem of variational inference in probabilistic models with both log-submodular and log-supermodular higher-order potentials.
Higher-Order Inference for Multi-Class Log-Supermodular Models
Higher-order models have been shown to be very useful for a plethora of computer vision tasks.
Scalable Variational Inference in Log-supermodular Models
We consider the problem of approximate Bayesian inference in log-supermodular models.
From MAP to Marginals: Variational Inference in Bayesian Submodular Models
Submodular optimization has found many applications in machine learning and beyond.
High-Dimensional Gaussian Process Bandits
Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain.
