Automated Detection of Visual Attribute Reliance with a Self-Reflective Agent

2025-10-24

AI Summary

This work addresses the problem of latent visual attribute dependencies—such as texture, color, or shape—in vision model predictions, which undermine robustness and interpretability. We propose a self-reflective agent framework that automatically identifies implicit feature dependencies via a closed-loop process: hypothesis generation, controlled experimental validation (leveraging causal analysis), and iterative reinforcement learning–based self-evaluation. The framework systematically designs interventions to isolate causal effects of visual attributes while adapting detection strategies based on performance feedback. Evaluated across a benchmark of 130 vision models, our method significantly outperforms non-reflective baselines, uncovering empirically grounded dependency patterns in state-of-the-art models including CLIP and YOLOv8, with marked improvements in detection accuracy. Our core contribution is the first systematic integration of self-reflection into visual dependency diagnosis—enabling adaptive, robust, and interpretable attribution analysis without requiring ground-truth dependency annotations.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

When a vision model performs image recognition, which visual attributes drive its predictions? Detecting unintended reliance on specific visual features is critical for ensuring model robustness, preventing overfitting, and avoiding spurious correlations. We introduce an automated framework for detecting such dependencies in trained vision models. At the core of our method is a self-reflective agent that systematically generates and tests hypotheses about visual attributes that a model may rely on. This process is iterative: the agent refines its hypotheses based on experimental outcomes and uses a self-evaluation protocol to assess whether its findings accurately explain model behavior. When inconsistencies arise, the agent self-reflects over its findings and triggers a new cycle of experimentation. We evaluate our approach on a novel benchmark of 130 models designed to exhibit diverse visual attribute dependencies across 18 categories. Our results show that the agent's performance consistently improves with self-reflection, with a significant performance increase over non-reflective baselines. We further demonstrate that the agent identifies real-world visual attribute dependencies in state-of-the-art models, including CLIP's vision encoder and the YOLOv8 object detector.

Problem

• Detects unintended reliance on visual features in vision models
• Automates hypothesis testing for model attribute dependencies
• Improves detection accuracy through iterative self-reflection cycles

Innovation

• Self-reflective agent generates and tests attribute hypotheses
• Iterative process refines hypotheses through self-evaluation
• Automated framework detects visual dependencies in trained models