A Chowdhury, J Rosenthal, J Waring, R Umeton - Informatics, 2021 - mdpi.com
Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and …
ZJ Yew, GH Lee - Proceedings of the IEEE/CVF conference …, 2020 - openaccess.thecvf.com
Abstract Iterative Closest Point (ICP) solves the rigid point cloud registration problem iteratively in two steps:(1) make hard assignments of spatially closest point …
We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match …
Y Li, R Bu, M Sun, W Wu, X Di… - Advances in neural …, 2018 - proceedings.neurips.cc
We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local …
B Ibarz, V Kurin, G Papamakarios… - Learning on graphs …, 2022 - proceedings.mlr.press
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge …
We introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycle …
Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult …
This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it …
Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream …