Weakly supervised object detection aims at reducing the amount of supervision
required to train detection models. Such models are traditionally learned from
images/videos labelled only with the object class and not the object bounding
box. In our work, we try to leverage not only the object class labels but also
the action labels associated with the data. We show that the action depicted in
the image/video can provide strong cues about the location of the associated
object. We learn a spatial prior for the object dependent on the action (e.g.
"ball" is closer to "leg of the person" in "kicking ball"), and incorporate
this prior to simultaneously train a joint object detection and action
classification model. We conducted experiments on both video datasets and image
datasets to evaluate the performance of our weakly supervised object detection
model. Our approach outperformed the current state-of-the-art (SOTA) method by
more than 6% in mAP on the Charades video dataset.Comment: CVPR'19 camera read