1 citations · 1 across the 3 of their papers we have counts for
4 papers
Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data
Thiago César Castilho Almeida, Gustavo Rosseto Letício, Lucas Pascotti Valem +2
In a data-driven world, efficiently organizing and mapping relationships between objects is crucial. Graphs are powerful tools for modeling these connections, being widely used in…
Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
Camila Piscioneri Magalhães, Lucas Pascotti Valem
While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming. Therefore, semi-supervised approaches such as Gra…
A Coreset Selection Framework with Ensemble Aggregation for Image Classification
Pedro Rocha Dantas, Lucas Pascotti Valem
The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training. Selecting representative training subsets, how…
Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation
Marina Chagas Bulach Gapski, Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio +3
Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature…