no code implementations • 24 May 2019 • Anusha Lalitha, Xinghan Wang, Osman Kilinc, Yongxi Lu, Tara Javidi, Farinaz Koushanfar
The proposed algorithm can be viewed as a Bayesian and peer-to-peer variant of federated learning in which each agent keeps a "posterior probability distribution" over a global model parameters.
no code implementations • 24 May 2019 • Yongxi Lu, Ziyao Tang, Tara Javidi
Partially annotated clips contain rich temporal contexts that can complement the sparse key frame annotations in providing supervision for model training.
2 code implementations • CVPR 2019 • Yue Meng, Yongxi Lu, Aman Raj, Samuel Sunarjo, Rui Guo, Tara Javidi, Gaurav Bansal, Dinesh Bharadia
SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error).
Ranked #62 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 24 Nov 2018 • Ziyao Tang, Yongxi Lu, Tara Javidi
One of the greatest challenges in the design of a real-time perception system for autonomous driving vehicles and drones is the conflicting requirement of safety (high prediction accuracy) and efficiency.
1 code implementation • CVPR 2017 • Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogerio Feris
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them.
4 code implementations • CVPR 2017 • Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, Rogerio Feris
We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e. g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e. g., top-left) manner.
1 code implementation • CVPR 2016 • Yongxi Lu, Tara Javidi, Svetlana Lazebnik
Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small.
no code implementations • 5 Oct 2015 • Yongxi Lu, Tara Javidi
Efficient generation of high-quality object proposals is an essential step in state-of-the-art object detection systems based on deep convolutional neural networks (DCNN) features.