Coaction discovery
Caiming Xiong, Jason J. Corso · 2012
We introduce a new problem called coaction discovery: the task of discovering and segmenting the common actions (coactions) between videos that may contain several actions. This paper presents an approach for coaction discovery; the key idea of our approach is to compute an action proposal map for each video based jointly on dynamic object-motion and static appearance semantics, and unsupervisedly cluster each video into atomic action clips, called actoms. Subsequently, we use a temporally coherent discriminative clustering framework for extracting the coactions. We apply our coaction discovery approach to two datasets and demonstrate convincing and superior performance to three baseline methods.