Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Jian, Xingchao"'
Autor:
She, Rui, Wang, Sijie, Kang, Qiyu, Zhao, Kai, Song, Yang, Tay, Wee Peng, Geng, Tianyu, Jian, Xingchao
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2024), Vancouver, Canada, 2024
Point cloud registration is a crucial technique in 3D computer vision with a wide range of applications. However, this task can be challenging, particularly in large fields of view with dynamic objects, environmental noise, or other perturbations. To
Externí odkaz:
http://arxiv.org/abs/2401.03167
The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts. Nonetheless, integrating additional sensors comes with elevated costs and may not be feasible fo
Externí odkaz:
http://arxiv.org/abs/2312.10616
Graphons are limit objects of sequences of graphs and are used to analyze the behavior of large graphs. Recently, graphon signal processing has been developed to study signal processing on large graphs. A major limitation of this approach is that any
Externí odkaz:
http://arxiv.org/abs/2312.08124
In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the convergence in the cut norm, usually used to study graphon sequences.
Externí odkaz:
http://arxiv.org/abs/2310.14683
Topological Signal Processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP via a generalized higher-order version of graphon, known as complexon.
Externí odkaz:
http://arxiv.org/abs/2309.07169
Graphons have traditionally served as limit objects for dense graph sequences, with the cut distance serving as the metric for convergence. However, sparse graph sequences converge to the trivial graphon under the conventional definition of cut dista
Externí odkaz:
http://arxiv.org/abs/2309.05260
In generalized graph signal processing (GGSP), the signal associated with each vertex in a graph is an element from a Hilbert space. In this paper, we study GGSP signal reconstruction as a kernel ridge regression (KRR) problem. By devising an appropr
Externí odkaz:
http://arxiv.org/abs/2308.06949
Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars. In this paper, we develop TSP theories for the case where observations belong to general abelian g
Externí odkaz:
http://arxiv.org/abs/2305.06899
In this paper, we propose a framework for graph signal processing using category theory. The aim is to generalize a few recent works on probabilistic approaches to graph signal processing, which handle signal and graph uncertainties.
Externí odkaz:
http://arxiv.org/abs/2302.12421