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.

中文翻译：

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GTNet：用于零样本目标检测的生成传输网络

我们提出了一个用于零镜头对象检测（ZSD）的生成传输网络（GTNet）。GTNet 由一个对象检测模块和一个知识转移模块组成。目标检测模块可以学习大规模的可见领域知识。知识转移模块利用特征合成器生成看不见的类特征，这些特征用于为对象检测模块训练新的分类层。为了将每个看不见的类的特征与类内方差和 IoU 方差合成，我们设计了一个 IoU 感知生成对抗网络（IoUGAN）作为特征合成器，它可以很容易地集成到 GTNet 中。具体来说，IoUGAN由三个单元模型组成：类特征生成单元（CFU）、前景特征生成单元（FFU）和背景特征生成单元（BFU）。CFU 以类语义嵌入为条件，以类内方差生成看不见的特征。FFU 和 BFU 将 IoU 方差添加到 CFU 的结果中，分别产生特定于类的前景和背景特征。我们在三个公共数据集上评估了我们的方法，结果表明我们的方法与最先进的 ZSD 方法相比表现良好。