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رؤى - Affordance Grounding
Leveraging CLIP's Implicit Affordance Knowledge for Zero-Shot Affordance Grounding
CLIP, a large pre-trained vision-language model, implicitly embeds valuable knowledge about how humans interact with objects, enabling zero-shot affordance grounding without the need for explicit supervision.
Leveraging Vision-Language Models to Enhance Affordance Grounding for In-the-Wild Objects
Affordance grounding can be significantly improved by leveraging the rich world knowledge embedded in large-scale vision-language models, enabling better generalization to novel objects and actions.
Interaction Relationship-aware Weakly Supervised Affordance Grounding for Intelligent Systems
INTRA, a novel weakly supervised affordance grounding framework, leverages interaction relationship-guided contrastive learning and text-conditioned affordance map generation to enable flexible and accurate grounding of multiple affordances on a single object, without requiring paired egocentric and exocentric images.
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