Zobrazeno 1 - 10
of 9 531
pro vyhledávání: '"Cai Hong"'
Autor:
Birkfellner, Birgit
Publikováno v:
Info DaF: Informationen Deutsch als Fremdsprache; March 2024, Vol. 51 Issue: 2-3 p66-69, 4p
Autor:
Letourneau, Pierre-David, Singh, Manish Kumar, Cheng, Hsin-Pai, Han, Shizhong, Shi, Yunxiao, Jones, Dalton, Langston, Matthew Harper, Cai, Hong, Porikli, Fatih
We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Notably, several recent alternative attention mechanisms, including Hyena
Externí odkaz:
http://arxiv.org/abs/2407.11306
Autor:
Li, Guang-Wei, Wang, Liang, Yuan, Hai-Long, Xin, Li-Ping, Wang, Jing, Wu, Chao, Li, Hua-Li, Haerken, Hasitieer, Wang, Wei-Hua, Cai, Hong-Bo, Han, Xu-Hui, Xu, Yang, Huang, Lei, Lu, Xiao-Meng, Bai, Jian-Ying, Wang, Xiang-Yu, Dai, Zi-Gao, Liang, En-Wei, Wei, Jian-Yan
Publikováno v:
2024, The Astrophysical Journal, Vol. 971, 114
M-type stars are the ones that flare most frequently, but how big their maximum flare energy can reach is still unknown. We present 163 flares from 162 individual M2 through L1-type stars that triggered the GWAC, with flare energies ranging from $10^
Externí odkaz:
http://arxiv.org/abs/2407.08183
In this paper, we propose a novel token selective attention approach, ToSA, which can identify tokens that need to be attended as well as those that can skip a transformer layer. More specifically, a token selector parses the current attention maps a
Externí odkaz:
http://arxiv.org/abs/2406.08816
Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex challenge. First, an optical flow model must be sufficiently lightweight
Externí odkaz:
http://arxiv.org/abs/2404.08135
Autor:
Jeong, Jisoo, Cai, Hong, Garrepalli, Risheek, Lin, Jamie Menjay, Hayat, Munawar, Porikli, Fatih
The scarcity of ground-truth labels poses one major challenge in developing optical flow estimation models that are both generalizable and robust. While current methods rely on data augmentation, they have yet to fully exploit the rich information av
Externí odkaz:
http://arxiv.org/abs/2403.18092
Autor:
Yasarla, Rajeev, Singh, Manish Kumar, Cai, Hong, Shi, Yunxiao, Jeong, Jisoo, Zhu, Yinhao, Han, Shizhong, Garrepalli, Risheek, Porikli, Fatih
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specific
Externí odkaz:
http://arxiv.org/abs/2403.12953
In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically, we first enhance a baseline convolutional depth completio
Externí odkaz:
http://arxiv.org/abs/2403.12202
This paper presents Neural Mesh Fusion (NMF), an efficient approach for joint optimization of polygon mesh from multi-view image observations and unsupervised 3D planar-surface parsing of the scene. In contrast to implicit neural representations, NMF
Externí odkaz:
http://arxiv.org/abs/2402.16739
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Anthrax is a global health concern, with cutaneous anthrax accounting for over 95% of cases and generally promising outcomes. Nonetheless, the absence of timely intervention can result in mortality rates of 10–40%. This research
Externí odkaz:
https://doaj.org/article/d9787e1b9d3e48c4bc07aeeac19839b9