23 citations · 42 across the 9 of their papers we have counts for
11 papers
Region-Aware CLS Token Augmentation for Fine-Grained Image Retrieval
Ian de Holanda Cavalcanti Bezerra, Vivek Trivedy, Lucas Pascotti Valem +1
Image retrieval methods often rely on a single global semantic descriptor extracted from an image, e.g., the [CLS] token in vision transformers. However, trying to squeeze all the…
Aggregating Neighbor Embedding Projection and Rank-Based Manifold Learning for Image Retrieval
Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio, Lucas Pascotti Valem +1
Content-based image retrieval (CBIR) has advanced significantly with deep learning, yet effectively ranking similar images remains challenging, particularly in high-dimensional fea…
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…
Beyond Topical Similarity: Contrastive Evidence Retrieval with Interpretable Attention Alignment in RAG
Francielle Vargas, João Robiatti, Diego Alves +6
Ensuring factuality and interpretability in RAG remains an open and urgent problem. We introduce Contrastive Evidence Rationale Attention (CERA), the first retrieval framework to e…
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…
Person Re-ID through Unsupervised Hypergraph Rank Selection and Fusion
Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette
Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals ac…
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification
Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Longin Jan Latecki
Due to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of l…
Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning
Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Longin Jan Latecki
Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true fo…