CRAFT: A Neuro-Symbolic Framework for Visual Functional Affordance Grounding

2025-07-18

AI Summary

This paper addresses visual functional affordance localization—the task of identifying objects capable of supporting specific actions (e.g., “cutting”) in fully unannotated scenes. We propose a neuro-symbolic reasoning framework that integrates ConceptNet commonsense knowledge, language model–derived semantic priors, and CLIP-based visual representations into an energy-driven iterative inference loop, enabling explicit alignment between symbolic logic and perceptual features. A differentiable energy function jointly optimizes visual evidence and commonsense constraints, ensuring transparent, goal-directed reasoning. Evaluated under multi-object, zero-label settings, our method significantly improves localization accuracy while providing traceable, interpretable decision rationales. Our key contribution is the first end-to-end integration of structured commonsense knowledge into visual reasoning—achieving both high performance and intrinsic interpretability—and thereby advancing trustworthy embodied intelligence.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

We introduce CRAFT, a neuro-symbolic framework for interpretable affordance grounding, which identifies the objects in a scene that enable a given action (e.g., "cut"). CRAFT integrates structured commonsense priors from ConceptNet and language models with visual evidence from CLIP, using an energy-based reasoning loop to refine predictions iteratively. This process yields transparent, goal-driven decisions to ground symbolic and perceptual structures. Experiments in multi-object, label-free settings demonstrate that CRAFT enhances accuracy while improving interpretability, providing a step toward robust and trustworthy scene understanding.

Problem

• Identify objects enabling specific actions in scenes
• Integrate commonsense knowledge with visual evidence
• Improve interpretability and accuracy in affordance grounding

Innovation

• Neuro-symbolic framework for affordance grounding
• Integrates ConceptNet, language models, and CLIP
• Energy-based reasoning loop for iterative refinement