The existing zero-shot detection approaches project visual features to the
semantic domain for seen objects, hoping to map unseen objects to their
corresponding semantics during inference. However, since the unseen objects are
never visualized during training, the detection model is skewed towards seen
content, thereby labeling unseen as background or a seen class. In this work,
we propose to synthesize visual features for unseen classes, so that the model
learns both seen and unseen objects in the visual domain. Consequently, the
major challenge becomes, how to accurately synthesize unseen objects merely
using their class semantics? Towards this ambitious goal, we propose a novel
generative model that uses class-semantics to not only generate the features
but also to discriminatively separate them. Further, using a unified model, we
ensure the synthesized features have high diversity that represents the
intra-class differences and variable localization precision in the detected
bounding boxes. We test our approach on three object detection benchmarks,
PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and
generalized settings, showing impressive gains over the state-of-the-art
methods. Our codes are available at
this https URL

提出了一种用于物体检测的零样本学习方法，通过合成视觉特征实现模型同时学习已知和未知的物体，并采用一种新颖的生成模型，使用类语义不仅生成特征，而且还对它们进行鉴别分离。在三个物体检测基准测试中表现优秀，超过现有技术方法。

零样本物体检测中综合未见过的物体

Synthesizing the Unseen for Zero-shot Object Detection

Most existing zero-shot learning methods consider the problem as a visual
semantic embedding one. Given the demonstrated capability of Generative
Adversarial Networks(GANs) to generate images, we instead leverage GANs to
imagine unseen categories from text descriptions and hence recognize novel
classes with no examples being seen. Specifically, we propose a simple yet
effective generative model that takes as input noisy text descriptions about an
unseen class (e.g.Wikipedia articles) and generates synthesized visual features
for this class. With added pseudo data, zero-shot learning is naturally
converted to a traditional classification problem. Additionally, to preserve
the inter-class discrimination of the generated features, a visual pivot
regularization is proposed as an explicit supervision. Unlike previous methods
using complex engineered regularizers, our approach can suppress the noise well
without additional regularization. Empirically, we show that our method
consistently outperforms the state of the art on the largest available
benchmarks on Text-based Zero-shot Learning.

利用生成对抗网络 (GANs) 想象从文本描述中无法见到的类别，生成目标类别的视觉特征，附加伪数据，使得零样本学习自然地转换为传统的分类问题，并提出视觉中心正则化以保持生成特征的跨类别区分能力。实验表明，该方法在基于文本的零样本学习的最大可用基准测试中始终优于现有技术水平。