  As we move towards large-scale object detection, it is unrealistic to expect
annotated training data, in the form of bounding box annotations around
objects, for all object classes at sufficient scale, and so methods capable of
unseen object detection are required. We propose a novel zero-shot method based
on training an end-to-end model that fuses semantic attribute prediction with
visual features to propose object bounding boxes for seen and unseen classes.
While we utilize semantic features during training, our method is agnostic to
semantic information for unseen classes at test-time. Our method retains the
efficiency and effectiveness of YOLOv2 for objects seen during training, while
improving its performance for novel and unseen objects. The ability of
state-of-art detection methods to learn discriminative object features to
reject background proposals also limits their performance for unseen objects.
We posit that, to detect unseen objects, we must incorporate semantic
information into the visual domain so that the learned visual features reflect
this information and leads to improved recall rates for unseen objects. We test
our method on PASCAL VOC and MS COCO dataset and observed significant
improvements on the average precision of unseen classes.
