arXiv:2509. 11218v2 Announce Type: replace-cross Abstract: Spatial transformations such as rotation and scale obscure the morphological cues needed for accurate image classification.
By Johann Schmidt, Sebastian Stober
arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.
By Camila Piscioneri Magalh\~aes, Lucas Pascotti Valem
arXiv:2602.15239v3 Announce Type: replace
Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for...
By Javier Porras-Valenzuela, Zhiyang Wang, Teresa Shang, Yusu Wang, Alejandro Ribeiro
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.
arXiv:2606. 17406v1 Announce Type: cross Abstract: Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes.
By Marina Chagas Bulach Gapski, Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio, Lucas Pascotti Valem, Daniel Carlos Guimar\~aes Pedronette, Mohand Said Allili
arXiv:2609.37015v1 Announce Type: new
Abstract: We propose RBF-GNN, a new pseudo-coordinate based graph neural network architecture that takes into account Euclidean, spherical or angular coordinates...
By Pawe{\l} Batorski, Abtin Pourhadi, Paul Swoboda