Content-based image retrieval (CBIR) has evolved significantly with the advent of deep learning models, yet effectively ranking similar images remains a challenging task, particularly in high-dimensional feature spaces where pairwise distance measures often fail to capture complex contextual relationships and the seman...
V. Kawai, G. Leticio, L. Valem et al.· Journal of the Brazilian Com...· 0 citations
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, however, remains challenging: individual sample contributions are unclear, and model behavior varies across datasets and runs. We address these...
GRaCE surpasses RaDE and Original Features across diverse datasets, including textual and image collections, excelling in retrieval, classification, and clustering tasks, considering state-of-the-art Transformer models as feature descriptors and Graph Convolutional Networks models in classification tasks.
Thiago César Castilho Almeida, G. Leticio, L. P. Valem et al.· IEEE International Joint Con...· 1 citation· ⚡1
Experimental results reveal that leveraging LLMs for graph refinement can improve classification accuracy, particularly for kNN graphs and some backbones, particularly for kNN graphs and some backbones.
Camila Piscioneri Magalhães, L. Valem· arXiv.org· 0 citations
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