no code implementations • 4 Feb 2024 • Wuxuan Jiang, Xiangjun Song, Shenbai Hong, Haijun Zhang, Wenxin Liu, Bo Zhao, Wei Xu, Yi Li
Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks.
no code implementations • 10 Oct 2023 • Wenxin Liu, Michael Fischer, Paul D. Yoo, Tobias Ritschel
Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception.
3 code implementations • ICCV 2021 • Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, Kui Ren
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.
no code implementations • ICCV 2021 • Chao Qu, Wenxin Liu, Camillo J. Taylor
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.
no code implementations • 6 Jul 2020 • Wenxin Liu, David Caruso, Eddy Ilg, Jing Dong, Anastasios I. Mourikis, Kostas Daniilidis, Vijay Kumar, Jakob Engel
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.
no code implementations • 20 Dec 2018 • Alex Zihao Zhu, Wenxin Liu, ZiYun Wang, Vijay Kumar, Kostas Daniilidis
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.