Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

• URL: http://arxiv.org/abs/2409.01814v1
• Date: Tue, 3 Sep 2024 11:54:36 GMT
• Title: Segmenting Object Affordances: Reproducibility and Sensitivity to Scale
• Authors: Tommaso Apicella, Alessio Xompero, Paolo Gastaldo, Andrea Cavallaro,
• Abstract summary: Methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task. We benchmark these methods under a reproducible setup on two single objects scenarios. Our analysis shows that models are not robust to scale variations when object resolutions differ from those in the training set.
• Score: 27.277739855754447
• License: http://creativecommons.org/licenses/by/4.0/
• 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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