We introduce and tackle the problem of zero-shot object detection (ZSD),
which aims to detect object classes which 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 motivate two background-aware approaches
for learning robust detectors. One of these models uses a fixed background
class and the other is based on iterative latent assignments. We also outline
the challenge associated with using a limited number of training classes and
propose a solution based on dense sampling of the semantic label space using
auxiliary data with a large number of categories. We propose novel splits of
two standard detection datasets - MSCOCO and VisualGenome, and present
extensive empirical results in both the traditional and generalized zero-shot
settings to highlight the benefits of the proposed methods. We provide useful
insights into the algorithm and conclude by posing some open questions to
encourage further research.

本文提出了一种解决零样本目标检测问题的方法，并通过使用视觉 - 语义嵌入，背景感知方法，以及使用大量类别语义标签的辅助数据来解决仅有少数训练类别的问题。在 MSCOCO 和 VisualGenome 两个标准检测数据集上进行实验，验证了该方法的有效性。