Zobrazeno 1 - 10
of 7 514
pro vyhledávání: '"Deng, Yu"'
We developed a robust solution for real-time 6D object detection in industrial applications by integrating FoundationPose, SAM2, and LightGlue, eliminating the need for retraining. Our approach addresses two key challenges: the requirement for an ini
Externí odkaz:
http://arxiv.org/abs/2409.19986
Autor:
Isaza, Paulina Toro, Nidd, Michael, Zheutlin, Noah, Ahn, Jae-wook, Bhatt, Chidansh Amitkumar, Deng, Yu, Mahindru, Ruchi, Franz, Martin, Florian, Hans, Roukos, Salim
Clients wishing to implement generative AI in the domain of IT Support and AIOps face two critical issues: domain coverage and model size constraints due to model choice limitations. Clients might choose to not use larger proprietary models such as G
Externí odkaz:
http://arxiv.org/abs/2409.13707
We provide a rigorous derivation of Boltzmann's kinetic equation from the hard-sphere system for rarefied gas, that is valid for arbitrarily long time as long as a solution to the Boltzmann equation exists. This is a crucial step in resolving Hilbert
Externí odkaz:
http://arxiv.org/abs/2408.07818
Autor:
Pang, Chao, Deng, Yu-Hao, Kheradmand, Ezat, Hagelsieb, Luis Moreno, Guo, Yujie, Cheyns, David, Geiregat, Pieter, Hens, Zeger, Van Thourhout, Dries
Silicon photonics faces a persistent challenge in extending photodetection capabilities beyond the 1.6 um wavelength range, primarily due to the lack of appropriate epitaxial materials. Colloidal quantum dots (QDs) present a promising solution here,
Externí odkaz:
http://arxiv.org/abs/2405.12376
Autor:
Meng, Kui, Li, Zeya, Chen, Peng, Ma, Xingyue, Huang, Junwei, Li, Jiayi, Qin, Feng, Qiu, Caiyu, Zhang, Yilin, Zhang, Ding, Deng, Yu, Yang, Yurong, Gu, Genda, Hwang, Harold Y., Xue, Qi-Kun, Cui, Yi, Yuan, Hongtao
Exploration of new dielectrics with large capacitive coupling is an essential topic in modern electronics when conventional dielectrics suffer from the leakage issue near breakdown limit. To address this looming challenge, we demonstrate that rare-ea
Externí odkaz:
http://arxiv.org/abs/2404.02011
In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocular videos guided by 3DMM, we employ pseudo multi-view videos to learn
Externí odkaz:
http://arxiv.org/abs/2403.13570
Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is lar
Externí odkaz:
http://arxiv.org/abs/2403.13111
Autor:
Sun, Wenjie, Wang, Zhichao, Hao, Bo, Yan, Shengjun, Sun, Haoying, Gu, Zhengbin, Deng, Yu, Nie, Yuefeng
Publikováno v:
Adv. Mater. 2024, 2401342
Since their discovery, the infinite-layer nickelates have been regarded as an appealing system for gaining deeper insights into high temperature superconductivity (HTSC). However, the synthesis of superconducting samples has been proved to be challen
Externí odkaz:
http://arxiv.org/abs/2401.15979
Autor:
Zhang, Yu, Zhang, Yunyi, Shen, Yanzhen, Deng, Yu, Popa, Lucian, Shwartz, Larisa, Zhai, ChengXiang, Han, Jiawei
Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such dat
Externí odkaz:
http://arxiv.org/abs/2401.13129