CRAFT: A Neuro-Symbolic Framework for Visual Functional Affordance Grounding
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 19 Jul 2025.
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References · 10
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2024GPT-4o System Cardcited by 4,588
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
- 2023CoTDet: Affordance Knowledge Prompting for Task Driven Object Detectioncited by 36
- 2020Robot Object Retrieval with Contextual Natural Language Queriescited by 26
- 2019What Object Should I Use? - Task Driven Object Detectioncited by 22
- 2024TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detectioncited by 21
- 2024What does CLIP know about peeling a banana?cited by 9
- 2023Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoningcited by 6
- 2024Discovering Object Attributes by Prompting Large Language Models with Perception-Action APIscited by 2
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