Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers
Visual affordances identify regions in an image with potential interactions, offering a novel paradigm for scene understanding. Recognizing affordances allows autonomous robots to act more naturally, could enhance human-robot interactions, enrich augmented reality systems, and benefit prosthetic vision devices. Accurate and localized prediction of affordance regions, rather than general saliency maps is crucial for these applications. We present a model for instance segmentation of affordances by adopting sample-based and ensembles approaches for uncertainty estimation. We extend an attention-based architecture for our novel task, showing with detailed ablation experiments the effects of ...
| Authors | Lorenzo Mur-Labadia, Ruben Martinez-Cantina, Jose J. Guerrero |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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