Zobrazeno 1 - 10
of 32 586
pro vyhledávání: '"LIU Jin"'
Recently, 3D Gaussian Splatting (3DGS) has garnered significant attention. However, the unstructured nature of 3DGS poses challenges for large-scale surface reconstruction from aerial images. To address this gap, we propose the first large-scale surf
Externí odkaz:
http://arxiv.org/abs/2409.00381
Diffusion-based text-to-image generation models have significantly advanced the field of art content synthesis. However, current portrait stylization methods generally require either model fine-tuning based on examples or the employment of DDIM Inver
Externí odkaz:
http://arxiv.org/abs/2408.05492
Vertical federated learning (VFL), where each participating client holds a subset of data features, has found numerous applications in finance, healthcare, and IoT systems. However, adversarial attacks, particularly through the injection of adversari
Externí odkaz:
http://arxiv.org/abs/2408.04310
Autor:
Zhang, Xiaoqing, Nie, Qiushi, Xiao, Zunjie, Zhao, Jilu, Wu, Xiao, Guo, Pengxin, Li, Runzhi, Liu, Jin, Wei, Yanjie, Pan, Yi
Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory
Externí odkaz:
http://arxiv.org/abs/2408.02906
Quantum-classical hybrid dynamics is crucial for accurately simulating complex systems where both quantum and classical behaviors need to be considered. However, coupling between classical and quantum degrees of freedom and the exponential growth of
Externí odkaz:
http://arxiv.org/abs/2408.00276
Simulation of physical systems is one of the most promising use cases of future digital quantum computers. In this work we systematically analyze the quantum circuit complexities of block encoding the discretized elliptic operators that arise extensi
Externí odkaz:
http://arxiv.org/abs/2407.18347
With the rapid advancements of large-scale text-to-image diffusion models, various practical applications have emerged, bringing significant convenience to society. However, model developers may misuse the unauthorized data to train diffusion models.
Externí odkaz:
http://arxiv.org/abs/2407.13252
In current benchmarks for evaluating large language models (LLMs), there are issues such as evaluation content restriction, untimely updates, and lack of optimization guidance. In this paper, we propose a new paradigm for the measurement of LLMs: Ben
Externí odkaz:
http://arxiv.org/abs/2407.07531
STOchastic Recursive Momentum (STORM)-based algorithms have been widely developed to solve one to $K$-level ($K \geq 3$) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient issue and achieve near-optima
Externí odkaz:
http://arxiv.org/abs/2407.05286
The increasing use of Large Language Models (LLMs) in software development has garnered significant attention from researchers assessing the quality of the code they generate. However, much of the research focuses on controlled datasets such as Human
Externí odkaz:
http://arxiv.org/abs/2406.19544