3,207 research outputs found
The Energy of Convolution of 2-Dimension Exponential Random Variables Base on HaarWavelet
In this paper, through wavelet methods, we obtain the energy of convolution of two-dimension exponential random variables and analyze its some properties of wavelet alternation, and we obtain some new results.Key words: Exponential random variables; Wavelet alternation; Convolution; Energ
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks
Deep regression is an important problem with numerous applications. These
range from computer vision tasks such as age estimation from photographs, to
medical tasks such as ejection fraction estimation from echocardiograms for
disease tracking. Semi-supervised approaches for deep regression are notably
under-explored compared to classification and segmentation tasks, however.
Unlike classification tasks, which rely on thresholding functions for
generating class pseudo-labels, regression tasks use real number target
predictions directly as pseudo-labels, making them more sensitive to prediction
quality. In this work, we propose a novel approach to semi-supervised
regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME),
which improves training by generating high-quality pseudo-labels and
uncertainty estimates for heteroscedastic regression. Given that aleatoric
uncertainty is only dependent on input data by definition and should be equal
for the same inputs, we present a novel uncertainty consistency loss for
co-trained models. Our consistency loss significantly improves uncertainty
estimates and allows higher quality pseudo-labels to be assigned greater
importance under heteroscedastic regression. Furthermore, we introduce a novel
variational model ensembling approach to reduce prediction noise and generate
more robust pseudo-labels. We analytically show our method generates higher
quality targets for unlabeled data and further improves training. Experiments
show that our method outperforms state-of-the-art alternatives on different
tasks and can be competitive with supervised methods that use full labels. Our
code is available at https://github.com/xmed-lab/UCVME.Comment: Accepted by AAAI2
Depth Estimation from Monocular Images and Sparse Radar Data
In this paper, we explore the possibility of achieving a more accurate depth
estimation by fusing monocular images and Radar points using a deep neural
network. We give a comprehensive study of the fusion between RGB images and
Radar measurements from different aspects and proposed a working solution based
on the observations. We find that the noise existing in Radar measurements is
one of the main key reasons that prevents one from applying the existing fusion
methods developed for LiDAR data and images to the new fusion problem between
Radar data and images. The experiments are conducted on the nuScenes dataset,
which is one of the first datasets which features Camera, Radar, and LiDAR
recordings in diverse scenes and weather conditions. Extensive experiments
demonstrate that our method outperforms existing fusion methods. We also
provide detailed ablation studies to show the effectiveness of each component
in our method.Comment: 9 pages, 6 figures, Accepted to 2020 IEEE International Conference on
Intelligent Robots and Systems (IROS 2020
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