Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
Therefore, semi-supervised approaches such as Graph Convolutional Networks (GCNs), which learn from both labeled and unlabeled data, have emerged as a promising solution. One of the primary challenges in applying GCNs to image classification is graph construction, since, unlike in citation networks or similar domains, images typically do not come with a predefined structural representation. For visual data, most studies construct graphs based on the similarity between feature vectors from pretrained deep learning backbones, typically by employing kNN or reciprocal kNN algorithms.
- ▪Therefore, semi-supervised approaches such as Graph Convolutional Networks (GCNs), which learn from both labeled and unlabeled data, have emerged as a promising solution.
- ▪One of the primary challenges in applying GCNs to image classification is graph construction, since, unlike in citation networks or similar domains, images typically do not come with a predefined structural representation.
- ▪For visual data, most studies construct graphs based on the similarity between feature vectors from pretrained deep learning backbones, typically by employing kNN or reciprocal kNN algorithms.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2607.09104 (cs) [Submitted on 10 Jul 2026] Title:Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification Authors:Camila Piscioneri Magalhães, Lucas Pascotti Valem View a PDF of the paper titled Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification, by Camila Piscioneri Magalh\~aes and Lucas Pascotti Valem View PDF HTML (experimental) Abstract:While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.
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