Affordance Prediction via Learned Object Attributes

By Tucker Hermans, James M. Rehg, Aaron F. Bobick. 2011

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Citations in the corpus

Learning human activities and object affordances from RGB-D videos (2012, background): “For instance, Sun et al. (2009) proposed a probabilistic graphical model that leverages visual object categorization for learning affordances and Hermans et al. (2011) proposed the use of physical and visual attributes as a mid-level representation for affordance prediction.”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, background): “These works take a recognition based approach to identify the semantic affordance labels [70], [71], [72].”
Grounded Human-Object Interaction Hotspots From Video (2018, methodology): “First, methods that consider affordances as properties of an object’s shape or appearance [36, 18, 24] fall short of modeling actual object use and manipulation.”
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars (2017, background): “We believe that the ability to generate room layouts in a controllable manner can benefit various computer vision areas, including but not limited to depth estimation [28, 29, 66, 71], surface normal estimation [3,28,128], semantic segmentation [17,74,87], reasoning about object-supporting relations”
SceneGrok (2014, background): “There is a variety of related work that has looked at using human-object interactions to improve pose estimation, object recognition, action classification, and other related problem in different communities [Fritz et al. 2006; Montesano et al. 2008; Stark et al. 2008; Sun et al. 2010; Hermans et al”