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Visual Affordance Prediction: Survey and Reproducibility
Tommaso Apicella, Alessio Xompero, Andrea Cavallaro
Affordances are the potential actions an agent can perform on an object, as observed by a camera. Visual affordance prediction is formulated differently for tasks such as grasping…
Learning Privacy from Visual Entities
Alessio Xompero, Andrea Cavallaro
Subjective interpretation and content diversity make predicting whether an image is private or public a challenging task. Graph neural networks combined with convolutional neural n…
Segmenting Object Affordances: Reproducibility and Sensitivity to Scale
Tommaso Apicella, Alessio Xompero, Paolo Gastaldo +1
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 segment…
Explaining models relating objects and privacy
Alessio Xompero, Myriam Bontonou, Jean-Michel Arbona +2
Accurately predicting whether an image is private before sharing it online is difficult due to the vast variety of content and the subjective nature of privacy itself. In this pape…
A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containers
Xavier Weber, Alessio Xompero, Andrea Cavallaro
Estimating the 6D pose and size of household containers is challenging due to large intra-class variations in the object properties, such as shape, size, appearance, and transparen…
Improving filling level classification with adversarial training
Apostolos Modas, Alessio Xompero, Ricardo Sanchez-Matilla +2
We investigate the problem of classifying - from a single image - the level of content in a cup or a drinking glass. This problem is made challenging by several ambiguities caused…