As we move toward 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; therefore, the 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 the state-of-theart 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 lead 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. 1 Although the number of annotations have increased in common detection datasets (e.g. 20 classes provided by PASCAL VOC [14] and 80 provided by the more recent MSCOCO [15]), it is substantially smaller relative to image classification [16] . 2 We choose YOLOv2 for concreteness. Our method is applicable to other architectures. See Sec.III.B 1051-8215
In contrast, and like REF , we attempt to improve localization performance by outputting high-confidence bounding boxes for unseen objects.
Zero Shot Detection
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Computer Science