4 papers · 1 filter
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…
Gaussian Rank-Based Neighborhood Degree for Graph Neural Networks in Image Classification
Rafael Mendonça Duarte, Jean Roberto Ponciano, Lucas Pascotti Valem
The exponential growth of data has intensified the gap between the availability of unlabeled data and the high cost of manual annotation. Graph Neural Networks (GNNs) have emerged…