348 citations · 1.5k across the 43 of their papers we have counts for
7 papers · 1 filter
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng +4
We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise th…
DeRF: Decomposed Radiance Fields
Daniel Rebain, Wei Jiang, Soroosh Yazdani +3
With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel views of a 3D scene with quality that fools the human eye. Yet, generating these images is ve…
Interpreting Stellar Spectra with Unsupervised Domain Adaptation
Teaghan O'Briain, Yuan-Sen Ting, Sébastien Fabbro +3
We discuss how to achieve mapping from large sets of imperfect simulations and observational data with unsupervised domain adaptation. Under the hypothesis that simulated and obser…
Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets
Teaghan O'Briain, Yuan-Sen Ting, Sébastien Fabbro +3
The advancements in stellar spectroscopy data acquisition have made it necessary to accomplish similar improvements in efficient data analysis techniques. Current automated methods…
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square pro…
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
Jongwon Choi, Kwang Moo Yi, Jihoon Kim +5
Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard…
Image Matching across Wide Baselines: From Paper to Practice
Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk +4
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as o…