Zobrazeno 1 - 10
of 586
pro vyhledávání: '"I.3.6"'
Autor:
Hepworth, K. J., Church, Christopher
Data visualizations are inherently rhetorical, and therefore bias-laden visual artifacts that contain both explicit and implicit arguments. The implicit arguments depicted in data visualizations are the net result of many seemingly minor decisions ab
Externí odkaz:
http://arxiv.org/abs/2411.17704
Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such as logist
Externí odkaz:
http://arxiv.org/abs/2410.22722
Autor:
Fulir, Juraj, Jeziorski, Natascha, Bosnar, Lovro, Hagen, Hans, Redenbach, Claudia, Gospodnetić, Petra, Herrfurth, Tobias, Trost, Marcus, Gischkat, Thomas
The use of machine learning (ML) methods for development of robust and flexible visual inspection system has shown promising. However their performance is highly dependent on the amount and diversity of training data. This is often restricted not onl
Externí odkaz:
http://arxiv.org/abs/2410.14844
Data acquisition in array signal processing (ASP) is costly, as high angular and range resolutions require large antenna apertures and wide frequency bandwidths. Data requirements grow multiplicatively with viewpoints and frequencies, increasing coll
Externí odkaz:
http://arxiv.org/abs/2410.19801
Explaining the decisions of AI has become vital for fostering appropriate user trust in these systems. This paper investigates explanations for a structured prediction task called ``text-to-SQL Semantic Parsing'', which translates a natural language
Externí odkaz:
http://arxiv.org/abs/2410.16283
We propose the first video diffusion framework for reference-based lineart video colorization. Unlike previous works that rely solely on image generative models to colorize lineart frame by frame, our approach leverages a large-scale pretrained video
Externí odkaz:
http://arxiv.org/abs/2409.12960
This paper presents a nested tracking framework for analyzing cycles in 2D force networks within granular materials. These materials are composed of interacting particles, whose interactions are described by a force network. Understanding the cycles
Externí odkaz:
http://arxiv.org/abs/2409.06476
Autor:
Sun, Mingyang, Kou, Dongliang, Yuan, Ruisheng, Yang, Dingkang, Zhai, Peng, Zhao, Xiao, Jiang, Yang, Li, Xiong, Li, Jingchen, Zhang, Lihua
In virtual Hand-Object Interaction (HOI) scenarios, the authenticity of the hand's deformation is important to immersive experience, such as natural manipulation or tactile feedback. Unrealistic deformation arises from simplified hand geometry, negle
Externí odkaz:
http://arxiv.org/abs/2409.05143
Autor:
Cai, Zeyu, Wang, Duotun, Liang, Yixun, Shao, Zhijing, Chen, Ying-Cong, Zhan, Xiaohang, Wang, Zeyu
Score Distillation Sampling (SDS) has emerged as a prevalent technique for text-to-3D generation, enabling 3D content creation by distilling view-dependent information from text-to-2D guidance. However, they frequently exhibit shortcomings such as ov
Externí odkaz:
http://arxiv.org/abs/2409.05099
Autor:
Schmitz, Tadea, Gerrits, Tim
Symmetric second-order tensors are fundamental in various scientific and engineering domains, as they can represent properties such as material stresses or diffusion processes in brain tissue. In recent years, several approaches have been introduced
Externí odkaz:
http://arxiv.org/abs/2408.08099