Zero-shot 2D Grounding with Novel Affordance Types

Haomeng Zhang; Raymond A. Yeh

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arXiv · 2608.08929

Zero-shot 2D Grounding with Novel Affordance Types

Haomeng Zhang·Raymond A. Yeh

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Abstract

2D affordance grounding aims to locate the region of an object that a human can interact with. Existing research focuses on recognizing affordance types seen during training and does not study models' ability to generalize to novel affordances, which is crucial for real-world applications. We propose the task of zero-shot 2D grounding with novel affordance types (NAT) and introduce the NAT benchmarks. We then propose AffordAnything, a training-free method that leverages segmentation cues, motivated by the strong correlation between affordance regions and object subparts. To further improve performance, we develop AffordAnything+, a trainable variant that learns to combine these cues. On the proposed AGD20K-NAT benchmark, our best model AffordAnything+ achieves a substantial improvement of 12.3% (absolute) in IoU@0.4 over the SOTA affordance grounding method, OOAL.

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Haomeng Zhang, Raymond A. Yeh. 2026-08-09. Zero-shot 2D Grounding with Novel Affordance Types. https://arxiv.org/abs/2608.08929

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