Zobrazeno 1 - 10
of 5 422
pro vyhledávání: '"Zhenliang An"'
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Recently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approaches like ControlNet introduce an extra network that learns to follow a
Externí odkaz:
http://arxiv.org/abs/2410.09400
Human Action Recognition (HAR) is a very crucial task in computer vision. It helps to carry out a series of downstream tasks, like understanding human behaviors. Due to the complexity of human behaviors, many highly valuable behaviors are not yet enc
Externí odkaz:
http://arxiv.org/abs/2408.13463
This paper introduces PowerInfer-2, a framework designed for high-speed inference of Large Language Models (LLMs) on smartphones, particularly effective for models whose sizes exceed the device's memory capacity. The key insight of PowerInfer-2 is to
Externí odkaz:
http://arxiv.org/abs/2406.06282
Vanilla pixel-level classifiers for semantic segmentation are based on a certain paradigm, involving the inner product of fixed prototypes obtained from the training set and pixel features in the test image. This approach, however, encounters signifi
Externí odkaz:
http://arxiv.org/abs/2405.06525
Semantic segmentation is an important task for numerous applications but it is still quite challenging to achieve advanced performance with limited computational costs. In this paper, we present CGRSeg, an efficient yet competitive segmentation frame
Externí odkaz:
http://arxiv.org/abs/2405.06228
Publikováno v:
Water Research, 2024, 263, 122142
Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potent
Externí odkaz:
http://arxiv.org/abs/2404.10324
Autor:
Gong, Sheng, Zhang, Yumin, Mu, Zhenliang, Pu, Zhichen, Wang, Hongyi, Yu, Zhiao, Chen, Mengyi, Zheng, Tianze, Wang, Zhi, Chen, Lifei, Wu, Xiaojie, Shi, Shaochen, Gao, Weihao, Yan, Wen, Xiang, Liang
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Boo
Externí odkaz:
http://arxiv.org/abs/2404.07181
Autor:
Zhang, Dingxi, Yuan, Yu-Jie, Chen, Zhuoxun, Zhang, Fang-Lue, He, Zhenliang, Shan, Shiguang, Gao, Lin
As XR technology continues to advance rapidly, 3D generation and editing are increasingly crucial. Among these, stylization plays a key role in enhancing the appearance of 3D models. By utilizing stylization, users can achieve consistent artistic eff
Externí odkaz:
http://arxiv.org/abs/2404.05220
Human-computer symbiosis is a crucial direction for the development of artificial intelligence. As intelligent systems become increasingly prevalent in our work and personal lives, it is important to develop strategies to support users across physica
Externí odkaz:
http://arxiv.org/abs/2403.18331