  Learning an object detector or retrieval requires a large data set with
manual annotations. Such data sets are expensive and time consuming to create
and therefore difficult to obtain on a large scale. In this work, we propose to
exploit the natural correlation in narrations and the visual presence of
objects in video, to learn an object detector and retrieval without any manual
labeling involved. We pose the problem as weakly supervised learning with noisy
labels, and propose a novel object detection paradigm under these constraints.
We handle the background rejection by using contrastive samples and confront
the high level of label noise with a new clustering score. Our evaluation is
based on a set of 11 manually annotated objects in over 5000 frames. We show
comparison to a weakly-supervised approach as baseline and provide a strongly
labeled upper bound.
