Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification

2026-08-28

AI Summary

该论文针对相似性评估和模型透明度问题，提出一种结合流形学习与基于排名的可解释图嵌入的新框架，生成上下文感知的、低维且高效的表示。

Abstract

The advances in visual information modeling and representation during the last decades are remarkable, mainly supported by Convolutional Neural Networks, Transformer-based, and Foundation Models. Despite this progress, critical challenges regarding the nature of similarity assessment and model transparency have been neglected. A primary concern is the Geometric Gap, where traditional pairwise measures fail to capture the intrinsic geometry of the dataset manifold. Furthermore, the Interpretability Gap persists, as representations often lack alignment with human cognition. Therefore, how to provide interpretability to representations while maintaining low dimensionality and high effectiveness in downstream tasks remains an open challenge. In this paper, we propose a novel unsupervised framework that integrates Manifold Learning strategies with Rank-based Interpretable Graph Embeddings. Our approach effectively bridges these gaps by first characterizing the contextual information of the dataset through manifold analysis and subsequently generating sparse, self-explainable embeddings. The proposed approach employs a flexible formulation, allowing different Manifold Learning and Representation Learning strategies. Extensive experimental evaluation across diverse datasets and features demonstrates that our Context-Aware representations not only provide intrinsic interpretability and dimensionality reduction but also maintain or enhance effectiveness in downstream tasks, specifically in image retrieval and semi-supervised classification using Graph Convolutional Networks (GCNs).

Problem

• Geometric Gap
• Interpretability Gap
• Manifold Learning
• Representation Learning
• Context-Aware

Innovation

• Manifold Learning
• Interpretable Graph Embeddings
• Context-Aware Representations
• Rank-based