6 papers
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…
A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes
Thiago César Castilho Almeida, Daniel Carlos Guimarães Pedronette
Recent topic models leverage pretrained embeddings, but neural architectures produce latent representations without grounding in specific texts, and clustering-based pipelines assi…
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…
AbstRAG: Learning to Abstract for Retrieval Problems
Lei Xu, Xin Quan, Daniel Pedronette +1
Retrieval-augmented generation often fails when the query, the document evidence, and the user's intent are expressed at different levels of abstraction. A query may ask about a cl…
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…
Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking
Francielle Vargas, Daniel Pedronette
This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by fine-tuning embedding…