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Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The sto- ryline of a video describes causal relationships between ac- tions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are repre- sented in terms of spatio-temporal constraints. We formu- late an Integer Programming framework for action recogni- tion and storyline extraction using the storyline model and visual groundings learned from training data.

Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos