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Weakly supervised learning of interactions between humans and objects

Alessandro Prest 1, 2 Cordelia Schmid 1 Vittorio Ferrari 2 
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We introduce a weakly supervised approach for learning human actions modeled as interactions between humans and objects. Our approach is human-centric: we first localize a human in the image and then determine the object relevant for the action and its spatial relation with the human. The model is learned automatically from a set of still images annotated only with the action label. Our approach relies on a human detector to initialize the model learning. For robustness to various degrees of visibility, we build a detector that learns to combine a set of existing part detectors. Starting from humans detected in a set of images depicting the action, our approach determines the action object and its spatial relation to the human. Its final output is a probabilistic model of the human-object interaction, i.e. the spatial relation between the human and the object. We present an extensive experimental evaluation on the sports action dataset from Gupta et al., the PASCAL Action 2010 dataset, and a new human-object interaction dataset.
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Submitted on : Monday, December 12, 2011 - 7:27:43 PM
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Alessandro Prest, Cordelia Schmid, Vittorio Ferrari. Weakly supervised learning of interactions between humans and objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34 (3), pp.601-614. ⟨10.1109/TPAMI.2011.158⟩. ⟨inria-00611482v4⟩



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