Segmenting Object Affordances: Reproducibility and Sensitivity to Scale
This work addresses reproducibility issues in affordance segmentation for computer vision researchers, but it is incremental as it focuses on benchmarking and analysis.
The authors benchmarked existing visual affordance segmentation methods under a reproducible setup on tabletop and hand-held container scenarios, finding that Mask2Former performed best but models lacked robustness to scale variations.
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.