  Typical textual descriptions that accompany online videos are 'weak': i.e.,
they mention the main concepts in the video but not their corresponding
spatio-temporal locations. The concepts in the description are typically
heterogeneous (e.g., objects, persons, actions). Certain location constraints
on these concepts can also be inferred from the description. The goal of this
paper is to present a generalization of the Indian Buffet Process (IBP) that
can (a) systematically incorporate heterogeneous concepts in an integrated
framework, and (b) enforce location constraints, for efficient classification
and localization of the concepts in the videos. Finally, we develop posterior
inference for the proposed formulation using mean-field variational
approximation. Comparative evaluations on the Casablanca and the A2D datasets
show that the proposed approach significantly outperforms other
state-of-the-art techniques: 24% relative improvement for pairwise concept
classification in the Casablanca dataset and 9% relative improvement for
localization in the A2D dataset as compared to the most competitive baseline.
