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Found 6 papers, 1 papers with code
Spin: An Efficient Secure Computation Framework with GPU Acceleration
Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks.
Neural Bounding
Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception.
Feature Importance-aware Transferable Adversarial Attacks
More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image.
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty
By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements.
TLIO: Tight Learned Inertial Odometry
We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates.
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
In this work, we propose a method that combines unsupervised deep learning predictions for optical flow and monocular disparity with a model based optimization procedure for instantaneous camera pose.
