Projects | Zero-Shot Object Detection

ECCV 2018 | We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes that are not observed during training. We work with a challenging set of object classes, not restricting ourselves to similar and/or fine-grained categories as in prior works on zero-shot classification. We present a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and propose two background-aware approaches for learning robust detectors. Finally, we propose novel splits of two standard detection datasets—MSCOCO and VisualGenome—and present extensive empirical results.