CONF Apicella_ICCV_ACVR_2023/IDIAP Affordance segmentation of hand-occluded containers from exocentric images Apicella, Tommaso Xompero, Alessio Ragusa, Edoardo Berta, Riccardo Cavallaro, Andrea Gastaldo, Paolo Affordances computer vision Convolutional Neural Networks Fuses Geometry Hand occlusion image segmentation training virtual reality Visualization Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2023 https://ieeexplore.ieee.org/document/10350499 URL 10.1109/ICCVW60793.2023.00204 doi 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.