Zero-shot object detection aims at incorporating class semantic vectors to
realize the detection of (both seen and) unseen classes given an unconstrained
test image. In this study, we reveal the core challenges in this research area:
how to synthesize robust region features (for unseen objects) that are as
intra-class diverse and inter-class separable as the real samples, so that
strong unseen object detectors can be trained upon them. To address these
challenges, we build a novel zero-shot object detection framework that contains
an Intra-class Semantic Diverging component and an Inter-class Structure
Preserving component. The former is used to realize the one-to-more mapping to
obtain diverse visual features from each class semantic vector, preventing
miss-classifying the real unseen objects as image backgrounds. While the latter
is used to avoid the synthesized features too scattered to mix up the
inter-class and foreground-background relationship. To demonstrate the
effectiveness of the proposed approach, comprehensive experiments on PASCAL
VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the
new state-of-the-art performance on PASCAL VOC and COCO and it is the first
study to carry out zero-shot object detection in remote sensing imagery.

研究零样本物体检测的核心挑战，以设计了一个含有内类语义分歧组件和类间结构保持组件的框架，展示了新型方法在 PASCAL VOC、COCO 和 DIOR 数据集上实现了最先进的性能，并首次在遥感影像中达到了零样本物体检测的研究。