Zobrazeno 1 - 10
of 1 563
pro vyhledávání: '"Yang Jiawei"'
Publikováno v:
Nanophotonics, Vol 11, Iss 13, Pp 3093-3100 (2022)
Epitaxial semiconductor quantum dots (QDs) have been demonstrated as on-demand entangled photon sources through biexciton–exciton (XX-X) cascaded radiative processes. However, perfect entangled photon emitters at the specific wavelengths of 880 nm
Externí odkaz:
https://doaj.org/article/b6170277edd64e248e6744817639eeb9
Publikováno v:
Kongzhi Yu Xinxi Jishu, Iss 2, Pp 120-124 (2022)
Contactors are widely used in power, distribution and power applications. Health of contactor is a key factor to the healthy operation of power system. In this paper, a dynamic model of armature movement in the process of contactor closing is establi
Externí odkaz:
https://doaj.org/article/b074c3f5313f4baa8cb03daf45efd1b2
Autor:
Yang, Jiawei, Huang, Jiahui, Chen, Yuxiao, Wang, Yan, Li, Boyi, You, Yurong, Sharma, Apoorva, Igl, Maximilian, Karkus, Peter, Xu, Danfei, Ivanovic, Boris, Wang, Yue, Pavone, Marco
We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often rely on per-scene optimization, dense observations across space and ti
Externí odkaz:
http://arxiv.org/abs/2501.00602
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
Autor:
Chen, Ziyu, Yang, Jiawei, Huang, Jiahui, de Lutio, Riccardo, Esturo, Janick Martinez, Ivanovic, Boris, Litany, Or, Gojcic, Zan, Fidler, Sanja, Pavone, Marco, Song, Li, Wang, Yue
We introduce OmniRe, a holistic approach for efficiently reconstructing high-fidelity dynamic urban scenes from on-device logs. Recent methods for modeling driving sequences using neural radiance fields or Gaussian Splatting have demonstrated the pot
Externí odkaz:
http://arxiv.org/abs/2408.16760
Autor:
Liang, Xun, Wang, Hanyu, Wang, Yezhaohui, Song, Shichao, Yang, Jiawei, Niu, Simin, Hu, Jie, Liu, Dan, Yao, Shunyu, Xiong, Feiyu, Li, Zhiyu
In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly complex requirements. Beyond avoiding misleading or inappropriate cont
Externí odkaz:
http://arxiv.org/abs/2408.12599
Remote sensing change detection (RSCD) aims to identify the changes of interest in a region by analyzing multi-temporal remote sensing images, and has an outstanding value for local development monitoring. Existing RSCD methods are devoted to context
Externí odkaz:
http://arxiv.org/abs/2406.13606
Autor:
Wang, Letian, Kim, Seung Wook, Yang, Jiawei, Yu, Cunjun, Ivanovic, Boris, Waslander, Steven L., Wang, Yue, Fidler, Sanja, Pavone, Marco, Karkus, Peter
We propose DistillNeRF, a self-supervised learning framework addressing the challenge of understanding 3D environments from limited 2D observations in outdoor autonomous driving scenes. Our method is a generalizable feedforward model that predicts a
Externí odkaz:
http://arxiv.org/abs/2406.12095
Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing weakly-supervised anomaly detect
Externí odkaz:
http://arxiv.org/abs/2406.09147
Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the ability of TSAD is limited by two key challenges: (i) the ability to mode
Externí odkaz:
http://arxiv.org/abs/2405.19823