  In this paper, we address the problem of spatio-temporal person retrieval
from multiple videos using a natural language query, in which we output a tube
(i.e., a sequence of bounding boxes) which encloses the person described by the
query. For this problem, we introduce a novel dataset consisting of videos
containing people annotated with bounding boxes for each second and with five
natural language descriptions. To retrieve the tube of the person described by
a given natural language query, we design a model that combines methods for
spatio-temporal human detection and multimodal retrieval. We conduct
comprehensive experiments to compare a variety of tube and text representations
and multimodal retrieval methods, and present a strong baseline in this task as
well as demonstrate the efficacy of our tube representation and multimodal
feature embedding technique. Finally, we demonstrate the versatility of our
model by applying it to two other important tasks.
