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arXiv · 2409.01814

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Tommaso Apicella·Alessio Xompero·Paolo Gastaldo·Andrea Cavallaro
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Abstract

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size datasets. However, experimental setups are often not reproducible, thus leading to unfair and inconsistent comparisons. In this work, we benchmark these methods under a reproducible setup on two single objects scenarios, tabletop without occlusions and hand-held containers, to facilitate future comparisons. We include a version of a recent architecture, Mask2Former, re-trained for affordance segmentation and show that this model is the best-performing on most testing sets of both scenarios. Our analysis shows that models are not robust to scale variations when object resolutions differ from those in the training set.

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Tommaso Apicella, Alessio Xompero, Paolo Gastaldo, Andrea Cavallaro. 2024-09-03. Segmenting Object Affordances: Reproducibility and Sensitivity to Scale. https://doi.org/10.1007/978-3-031-92591-7_18

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