Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?

Understand

Affordances are the possibilities of actions the environment offers to the individual.

• Ordinary objects (hammer, knife) usually have many affordances (grasping, pounding, cutting), and detecting these allow artificial agents to understand what are their possibilities in the environment, with obvious application in Robotics.
• Proposed benchmarks and state-of-the-art prediction models for supervised affordance segmentation are usually modifications of popular object segmentation models such as Mask R-CNN.
• We observe that theoretically, these popular object segmentation methods should be sufficient for detecting affordances masks.