377 research outputs found
Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion
Modern automotive vehicles are often equipped with a budget commercial
rolling shutter camera. These devices often produce distorted images due to the
inter-row delay of the camera while capturing the image. Recent methods for
monocular rolling shutter motion compensation utilize blur kernel and the
straightness property of line segments. However, these methods are limited to
handling rotational motion and also are not fast enough to operate in real
time. In this paper, we propose a minimal solver for the rolling shutter motion
compensation which assumes known vertical direction of the camera. Thanks to
the Ackermann motion model of vehicles which consists of only two motion
parameters, and two parameters for the simplified depth assumption that lead to
a 4-line algorithm. The proposed minimal solver estimates the rolling shutter
camera motion efficiently and accurately. The extensive experiments on real and
simulated datasets demonstrate the benefits of our approach in terms of
qualitative and quantitative results.Comment: Submitted to WACV 201
Contrastive Learning for Lifted Networks
In this work we address supervised learning of neural networks via lifted
network formulations. Lifted networks are interesting because they allow
training on massively parallel hardware and assign energy models to
discriminatively trained neural networks. We demonstrate that the training
methods for lifted networks proposed in the literature have significant
limitations and show how to use a contrastive loss to address those
limitations. We demonstrate that this contrastive training approximates
back-propagation in theory and in practice and that it is superior to the
training objective regularly used for lifted networks.Comment: 9 pages, BMVC 201
Fully Variational Noise-Contrastive Estimation
By using the underlying theory of proper scoring rules, we design a family of
noise-contrastive estimation (NCE) methods that are tractable for latent
variable models. Both terms in the underlying NCE loss, the one using data
samples and the one using noise samples, can be lower-bounded as in variational
Bayes, therefore we call this family of losses fully variational
noise-contrastive estimation. Variational autoencoders are a particular example
in this family and therefore can be also understood as separating real data
from synthetic samples using an appropriate classification loss. We further
discuss other instances in this family of fully variational NCE objectives and
indicate differences in their empirical behavior.Comment: SCIA 2023, 13 page
Driving to Opportunity: Understanding the Links among Transportation Access, Residential Outcomes, and Economic Opportunity for Housing Voucher Recipients
In the 1990s and early 2000s, the Department of Housing and Urban Development sponsored two major experiments to test whether housing choice vouchers propelled low-income households into greater economic security, the Moving to Opportunity for Fair Housing program (MTO) and the Welfare to Work Voucher program (WTW). Using data from these programs, this study examines differences in residential location and employment outcomes between voucher recipients with access to automobiles and those without. Overall, the findings underscore the positive role of automobiles in outcomes for housing voucher participants
Pareto Meets Huber: Efficiently Avoiding Poor Minima in Robust Estimation
International audienceRobust cost optimization is the task of fitting parameters to data points containing outliers. In particular, we focus on large-scale computer vision problems, such as bundle adjustment , where Non-Linear Least Square (NLLS) solvers are the current workhorse. In this context, NLLS-based state of the art algorithms have been designed either to quickly improve the target objective and find a local minimum close to the initial value of the parameters, or to have a strong ability to avoid poor local minima. In this paper, we propose a novel algorithm relying on multi-objective optimization which allows to match those two properties. We experimentally demonstrate that our algorithm has an ability to avoid poor local minima that is on par with the best performing algorithms with a faster decrease of the target objective
Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data
X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data
- …