%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Apicella_ECCVW_2024,
         author = {Apicella, Tommaso and Xompero, Alessio and Gastaldo, Paolo and Cavallaro, Andrea},
       keywords = {Affordances, Benchmarking, semantic segmentation},
       projects = {Idiap},
          title = {Segmenting Object Affordances: Reproducibility and Sensitivity to Scale},
      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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2024/Apicella_ECCVW_2024.pdf}
}