Affordance segmentation of hand-occluded containers from exocentric images
This work addresses the specific problem of affordance segmentation under hand-occlusion for robotics or human-computer interaction applications, representing an incremental improvement over prior methods.
The paper tackles the problem of visual affordance segmentation for hand-held containers in exocentric images, addressing challenges from occlusions and geometry variety by proposing a model with auxiliary branches for separate processing of object and hand regions, which achieves better segmentation and generalization than existing models.
Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geometry and physical properties of these surfaces as well as occlusions. In this paper, we focus on occlusions of an object that is hand-held by a person manipulating it. To address this challenge, we propose an affordance segmentation model that uses auxiliary branches to process the object and hand regions separately. The proposed model learns affordance features under hand-occlusion by weighting the feature map through hand and object segmentation. To train the model, we annotated the visual affordances of an existing dataset with mixed-reality images of hand-held containers in third-person (exocentric) images. Experiments on both real and mixed-reality images show that our model achieves better affordance segmentation and generalisation than existing models.