Search Results for author:
Found 11 papers, 8 papers with code
Data-Driven, Soft Alignment of Functional Data Using Shapes and Landmarks
Alignment or registration of functions is a fundamental problem in statistical analysis of functions and shapes.
Adaptive Feature Interpolation for Low-Shot Image Generation
Training of generative models especially Generative Adversarial Networks can easily diverge in low-data setting.
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector
Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10. 44%, 5. 69%, 5. 97% mAP respectively on the official KITTI benchmark.
Statistical Shape Analysis of Brain Arterial Networks (BAN)
This framework is then used to generate shape summaries of BANs from 92 subjects, and to study the effects of age and gender on shapes of BAN components.
Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs
This paper utilizes a quotient structure to develop efficient algorithms for computing these quantities, leading to useful statistical tools, including principal component analysis and analytical statistical testing and modeling of graphical shapes.
A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data
Complex analyses involving multiple, dependent random quantities often lead to graphical models - a set of nodes denoting variables of interest, and corresponding edges denoting statistical interactions between nodes.
Group-wise Correlation Stereo Network
Previous works built cost volumes with cross-correlation or concatenation of left and right features across all disparity levels, and then a 2D or 3D convolutional neural network is utilized to regress the disparity maps.
Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize
Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any supervision in the form of ground truth disparity or depth.
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving.
Neural Network Encapsulation
Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers.
