Segmenting Object Affordances: Reproducibility and Sensitivity to Scale
| Type of publication: | Conference paper |
| Citation: | Apicella_ECCVW_2024 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of the European Conference on Computer Vision (ECCV) Workshops |
| Year: | 2024 |
| 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. |
| Keywords: | Affordances, Benchmarking, semantic segmentation |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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