Zobrazeno 1 - 10
of 13 456
pro vyhledávání: '"GAO, JUN"'
Autor:
Lu, Yifan, Ren, Xuanchi, Yang, Jiawei, Shen, Tianchang, Wu, Zhangjie, Gao, Jun, Wang, Yue, Chen, Siheng, Chen, Mike, Fidler, Sanja, Huang, Jiahui
We present InfiniCube, a scalable method for generating unbounded dynamic 3D driving scenes with high fidelity and controllability. Previous methods for scene generation either suffer from limited scales or lack geometric and appearance consistency a
Externí odkaz:
http://arxiv.org/abs/2412.03934
Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer. However, when transitioning to vision-language models (VLMs), their text-only rationales st
Externí odkaz:
http://arxiv.org/abs/2411.19488
For a positive real number $p$, the $p$-norm $\left\lVert G \right\rVert_p$ of a graph $G$ is the sum of the $p$-th powers of all vertex degrees. We study the maximum $p$-norm $\mathrm{ex}_{p}(n,F)$ of $F$-free graphs on $n$ vertices, focusing on the
Externí odkaz:
http://arxiv.org/abs/2411.15579
A graph $G$ is $(c,t)$-sparse if for every pair of vertex subsets $A,B\subset V(G)$ with $|A|,|B|\geq t$, $e(A,B)\leq (1-c)|A||B|$. In this paper we prove that for every $c>0$ and integer $\ell$, there exists $C>1$ such that if an $n$-vertex graph $G
Externí odkaz:
http://arxiv.org/abs/2411.12659
We propose an effective method for inserting adapters into text-to-image foundation models, which enables the execution of complex downstream tasks while preserving the generalization ability of the base model. The core idea of this method is to opti
Externí odkaz:
http://arxiv.org/abs/2410.22901
The detection of malicious social bots has become a crucial task, as bots can be easily deployed and manipulated to spread disinformation, promote conspiracy messages, and more. Most existing approaches utilize graph neural networks (GNNs)to capture
Externí odkaz:
http://arxiv.org/abs/2410.05356
Autor:
Fu, Xueming, Li, Yingtai, Tang, Fenghe, Li, Jun, Zhao, Mingyue, Teng, Gao-Jun, Zhou, S. Kevin
Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often require many projections, while reconstruction from sparse-view X-ray pro
Externí odkaz:
http://arxiv.org/abs/2410.00404
Autor:
Shen, Tianchang, Li, Zhaoshuo, Law, Marc, Atzmon, Matan, Fidler, Sanja, Lucas, James, Gao, Jun, Sharp, Nicholas
Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or deformation of a template, or as a disordered triangle soup lacking l
Externí odkaz:
http://arxiv.org/abs/2409.20562
Autor:
Gao, Jun, Krishna, Govind, Yeung, Edith, Yu, Lingxi, Gangopadhyay, Sayan, Chan, Kai-Sum, Huang, Chiao-Tzu, Descamps, Thomas, Reimer, Michael E., Poole, Philip J., Dalacu, Dan, Zwiller, Val, Elshaari, Ali W.
Coherent control of single photon sources is a key requirement for the advancement of photonic quantum technologies. Among them, nanowire-based quantum dot sources are popular due to their potential for on-chip hybrid integration. Here we demonstrate
Externí odkaz:
http://arxiv.org/abs/2409.14964
This paper describes the winning solutions of all tasks in Meta KDD Cup 24 from db3 team. The challenge is to build a RAG system from web sources and knowledge graphs. We are given multiple sources for each query to help us answer the question. The C
Externí odkaz:
http://arxiv.org/abs/2410.00005