Spatially dense self-supervised learning is a rapidly growing problem do...
In the past years, the application of neural networks as an alternative ...
While recent supervised methods for reference-based object counting cont...
Identifying the causal variables of an environment and how to intervene ...
In this paper we address the task of finding representative subsets of p...
The current dominant paradigm when building a machine learning model is ...
This work aims to improve the efficiency of vision transformers (ViT). W...
Multi-modal retrieval is an important problem for many applications, suc...
Large-scale pretrained models, especially those trained from vision-lang...
Diffusion models have demonstrated remarkable progress in image generati...
Self-supervised visual representation learning has recently attracted
si...
Causal representation learning is the task of identifying the underlying...
The growing capability and availability of generative language models ha...
Progress in self-supervised learning has brought strong general image
re...
The goal of this paper is to bypass the need for labelled examples in
fe...
Understanding the latent causal factors of a dynamical system from visua...
What can neural networks learn about the visual world from a single imag...
Computer vision has long relied on ImageNet and other large datasets of
...
Hateful memes pose a unique challenge for current machine learning syste...
In video transformers, the time dimension is often treated in the same w...
We tackle the problem of learning object detectors without supervision.
...
The quality of the image representations obtained from self-supervised
l...
Privacy considerations and bias in datasets are quickly becoming
high-pr...
The capabilities of natural language models trained on large-scale data ...
A large part of the current success of deep learning lies in the
effecti...
Self-supervised learning has advanced rapidly, with several results beat...
State-of-the-art methods for unsupervised representation learning can tr...