Zobrazeno 1 - 10
of 2 504
pro vyhledávání: '"I.4.8"'
In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents significant challenges due to
Externí odkaz:
http://arxiv.org/abs/2411.11029
Trees continue to fascinate with their natural beauty and as engineering masterpieces optimal with respect to several independent criteria. Pythagorean tree is a well-known fractal design that realistically mimics the natural tree branching structure
Externí odkaz:
http://arxiv.org/abs/2411.08024
This paper aims to reconstruct hundreds of people's 3D poses, shapes, and locations from a single image with unknown camera parameters. Due to the small and highly varying 2D human scales, depth ambiguity, and perspective distortion, no existing meth
Externí odkaz:
http://arxiv.org/abs/2411.06232
3D geometric shape completion hinges on representation learning and a deep understanding of geometric data. Without profound insights into the three-dimensional nature of the data, this task remains unattainable. Our work addresses this challenge of
Externí odkaz:
http://arxiv.org/abs/2411.05419
Our study introduces ResNet-L2 (RL2), a novel metric for evaluating generative models and image quality in histopathology, addressing limitations of traditional metrics, such as Frechet inception distance (FID), when the data is scarce. RL2 leverages
Externí odkaz:
http://arxiv.org/abs/2411.01034
Autor:
Farazi, Mohammad, Wang, Yalin
Utilizing patch-based transformers for unstructured geometric data such as polygon meshes presents significant challenges, primarily due to the absence of a canonical ordering and variations in input sizes. Prior approaches to handling 3D meshes and
Externí odkaz:
http://arxiv.org/abs/2411.00164
Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining the potential of machine learning models due to the time-intensive nature of manual annotations. This work introduces a novel approach that
Externí odkaz:
http://arxiv.org/abs/2410.24116
Autor:
Rodriguez, Pau, Blaas, Arno, Klein, Michal, Zappella, Luca, Apostoloff, Nicholas, Cuturi, Marco, Suau, Xavier
The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To address these issues, recent works have proposed to control model generati
Externí odkaz:
http://arxiv.org/abs/2410.23054
Computer-aided diagnosis (CAD) is today considered a vital tool in the field of biological image categorization, segmentation, and other related tasks. The current breakthrough in computer vision algorithms and deep learning approaches has substantia
Externí odkaz:
http://arxiv.org/abs/2410.14833
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models are tricky to efficiently train and often struggle to generalize beyond their training sce
Externí odkaz:
http://arxiv.org/abs/2410.13002