  We introduce One-shot Open Affordance Learning (OOAL), where a model is
trained with just one example per base object category, but is expected to
identify novel objects and affordances. While vision-language models excel at
recognizing novel objects and scenes, they often struggle to understand finer
levels of granularity such as affordances. To handle this issue, we conduct a
comprehensive analysis of existing foundation models, to explore their inherent
understanding of affordances and assess the potential for data-limited
affordance learning. We then propose a vision-language framework with simple
and effective designs that boost the alignment between visual features and
affordance text embeddings. Experiments on two affordance segmentation
benchmarks show that the proposed method outperforms state-of-the-art models
with less than 1% of the full training data, and exhibits reasonable
generalization capability on unseen objects and affordances.
