  We propose a Generative Transfer Network (GTNet) for zero shot object
detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge
Transfer Module. The Object Detection Module can learn large-scale seen domain
knowledge. The Knowledge Transfer Module leverages a feature synthesizer to
generate unseen class features, which are applied to train a new classification
layer for the Object Detection Module. In order to synthesize features for each
unseen class with both the intra-class variance and the IoU variance, we design
an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature
synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN
consists of three unit models: Class Feature Generating Unit (CFU), Foreground
Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU).
CFU generates unseen features with the intra-class variance conditioned on the
class semantic embeddings. FFU and BFU add the IoU variance to the results of
CFU, yielding class-specific foreground and background features, respectively.
We evaluate our method on three public datasets and the results demonstrate
that our method performs favorably against the state-of-the-art ZSD approaches.
