Papers › Weakly Supervised Affordance Detection
Weakly Supervised Affordance Detection
Johann Sawatzky, Abhilash Srikantha, Juergen Gall
Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convo- lutional neural network for multilabel affordance segmen- tation. We also propose an approach to train the network from very few keypoint annotations. Our approach achieves a higher affordance detection accuracy than other weakly supervised methods that also rely on keypoint annotations or image annotations as weak supervision.
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