Weakly Supervised Object Detection (WSOD) with only image-level annotation
has recently attracted wide attention. Many existing methods ignore the
inter-image relationship of instances which share similar characteristics while
can certainly be determined not to belong to the same category. Therefore, in
order to make full use of the weak label, we propose the Negative Prototypes
Guided Contrastive learning (NPGC) architecture. Firstly, we define Negative
Prototype as the proposal with the highest confidence score misclassified for
the category that does not appear in the label. Unlike other methods that only
utilize category positive feature, we construct an online updated global
feature bank to store both positive prototypes and negative prototypes.
Meanwhile, we propose a pseudo label sampling module to mine reliable instances
and discard the easily misclassified instances based on the feature similarity
with corresponding prototypes in global feature bank. Finally, we follow the
contrastive learning paradigm to optimize the proposal's feature representation
by attracting same class samples closer and pushing different class samples
away in the embedding space. Extensive experiments have been conducted on
VOC07, VOC12 datasets, which shows that our proposed method achieves the
state-of-the-art performance.

利用弱监督图像级别注释进行弱监督目标检测（WSOD）近年来引起了广泛关注。本文提出了负样本原型引导的对比学习（NPGC）架构，通过定义负样本原型、构建全局特征库、采样可靠实例以及在嵌入空间中进行对比学习来优化提案的特征表示，实现了最先进的检测性能。