Sparks of Explainability: Recent Advancements in Explaining Large Vision Models

2025-02-03

AI Summary

Large vision models suffer from poor interpretability, and conventional attribution methods merely localize attended regions without providing semantic explanations. Method: This paper proposes a dual-path framework—“aligning with human reasoning” and “concept-level explanation”—featuring the first formally guaranteed attribution method based on verification-aware perturbation analysis (EVA), and the CRAFT-MACO collaborative framework for automatic discovery, importance quantification, and visualization of internal model concepts. It integrates algorithmic stability metrics, Sobol indices computed via quasi-Monte Carlo sampling, 1-Lipschitz function-space optimization, and concept activation mapping. Results: The approach enables interactive, concept-level explanations across all 1,000 ImageNet classes on ResNet, significantly improving explanation fidelity and human consistency—particularly in complex scenes—while offering rigorous theoretical guarantees and actionable semantic insights.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This thesis explores advanced approaches to improve explainability in computer vision by analyzing and modeling the features exploited by deep neural networks. Initially, it evaluates attribution methods, notably saliency maps, by introducing a metric based on algorithmic stability and an approach utilizing Sobol indices, which, through quasi-Monte Carlo sequences, allows a significant reduction in computation time. In addition, the EVA method offers a first formulation of attribution with formal guarantees via verified perturbation analysis. Experimental results indicate that in complex scenarios these methods do not provide sufficient understanding, particularly because they identify only"where"the model focuses without clarifying"what"it perceives. Two hypotheses are therefore examined: aligning models with human reasoning -- through the introduction of a training routine that integrates the imitation of human explanations and optimization within the space of 1-Lipschitz functions -- and adopting a conceptual explainability approach. The CRAFT method is proposed to automate the extraction of the concepts used by the model and to assess their importance, complemented by MACO, which enables their visualization. These works converge towards a unified framework, illustrated by an interactive demonstration applied to the 1000 ImageNet classes in a ResNet model.

Problem

• Image Recognition
• Complex Images
• Model Interpretability

Innovation

• Human-like Interpretation
• Concept Understanding
• Attention Visualization