Token-Based Affordance Grounding with Large Vision-Language Models
📰 ArXiv cs.AI
Learn to ground affordances in images using large vision-language models for improved physical intelligence and embodied perception
Action Steps
- Apply token-based affordance grounding to images using large vision-language models to localize action-supporting regions
- Use weakly supervised learning with action labels from exocentric images to train the model
- Configure the model to distinguish between semantically similar actions and handle visually ambiguous images
- Test the model on a dataset with co-occurring actions to evaluate its performance
- Compare the results with previous studies to assess the improvement in affordance grounding
Who Needs to Know This
Computer vision engineers and researchers can benefit from this technique to improve their models' ability to understand image regions that support specific actions
Key Insight
💡 Token-based affordance grounding can effectively localize image regions that support specific actions, even in visually ambiguous images
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🔍 Improve physical intelligence and embodied perception with token-based affordance grounding using large vision-language models! #AI #ComputerVision
Key Takeaways
Learn to ground affordances in images using large vision-language models for improved physical intelligence and embodied perception
Full Article
Title: Token-Based Affordance Grounding with Large Vision-Language Models
Abstract:
arXiv:2607.03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception. Previous studies have primarily relied on weakly supervised learning with action labels from exocentric images. However, these methods often struggle with visually ambiguous exocentric images containing co-occurring actions; moreover, they fail to distinguish semantically similar actio
Abstract:
arXiv:2607.03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception. Previous studies have primarily relied on weakly supervised learning with action labels from exocentric images. However, these methods often struggle with visually ambiguous exocentric images containing co-occurring actions; moreover, they fail to distinguish semantically similar actio
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