Self-supervised monocular depth estimation (SS-MDE) has the potential to...
Graph convolutional networks (GCNs) enable end-to-end learning on graph
...
Diffusion models excel at generating photorealistic images from text-que...
This paper discusses the results for the second edition of the Monocular...
We present a new approach for synthesizing novel views of people in new
...
We revisit the problem of fair principal component analysis (PCA), where...
In recent years fairness in machine learning (ML) has emerged as a highl...
Recent years have seen a surge of interest in learning high-level causal...
This paper summarizes the results of the first Monocular Depth Estimatio...
This paper presents an open and comprehensive framework to systematicall...
Since out-of-distribution generalization is a generally ill-posed proble...
While self-supervised learning has enabled effective representation lear...
We show that deep neural networks that satisfy demographic parity do so
...
Estimating a semantically segmented bird's-eye-view (BEV) map from a sin...
Algorithmic fairness is frequently motivated in terms of a trade-off in ...
We present a new approach for synthesizing novel views of people in new
...
We approach instantaneous mapping, converting images to a top-down view ...
An important component for generalization in machine learning is to unco...
We initiate the study of fairness for ordinal regression, or ordinal
cla...
We present a novel form of explanation for Reinforcement Learning, based...
This article identifies a critical incompatibility between European noti...
Explaining sophisticated machine-learning based systems is an important ...
We present a simple regularisation of Adversarial Perturbations based up...
We present a novel data-driven regularizer for weakly-supervised learnin...
We introduce the first approach to solve the challenging problem of
unsu...
This paper proposes new search algorithms for counterfactual explanation...
Recent work on interpretability in machine learning and AI has focused o...
We propose a CNN-based approach for multi-camera markerless motion captu...
When an individual purchases a home, they simultaneously purchase its
st...
Most approaches in algorithmic fairness constrain machine learning metho...
In this work, we argue for the importance of causal reasoning in creatin...
Submodular extensions of an energy function can be used to efficiently
c...
There has been much discussion of the right to explanation in the EU Gen...
We demonstrate the use of shape-from-shading (SfS) to improve both the
q...
Deep generative models provide powerful tools for distributions over
com...
Machine learning can impact people with legal or ethical consequences wh...
We propose a unified formulation for the problem of 3D human pose estima...
We propose a novel Linear Program (LP) based formula- tion for solving j...
Detection of groups of interacting people is a very interesting and usef...
Markov Networks are widely used through out computer vision and machine
...
Submodular function minimization is a key problem in a wide variety of
a...