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。

基于负样本原型的对比学习用于无监督目标检测

Negative Prototypes Guided Contrastive Learning for WSOD

Single image-level annotations only correctly describe an often small subset
of an image's content, particularly when complex real-world scenes are
depicted. While this might be acceptable in many classification scenarios, it
poses a significant challenge for applications where the set of classes differs
significantly between training and test time. In this paper, we take a closer
look at the implications in the context of $\textit{few-shot learning}$.
Splitting the input samples into patches and encoding these via the help of
Vision Transformers allows us to establish semantic correspondences between
local regions across images and independent of their respective class. The most
informative patch embeddings for the task at hand are then determined as a
function of the support set via online optimization at inference time,
additionally providing visual interpretability of `$\textit{what matters
most}$' in the image. We build on recent advances in unsupervised training of
networks via masked image modelling to overcome the lack of fine-grained labels
and learn the more general statistical structure of the data while avoiding
negative image-level annotation influence, $\textit{aka}$ supervision collapse.
Experimental results show the competitiveness of our approach, achieving new
state-of-the-art results on four popular few-shot classification benchmarks for
$5$-shot and $1$-shot scenarios.

通过将输入样本分割成补丁，并借助 Vision Transformers 对其进行编码，从而在图像的局部区域之间建立语义对应关系，而不受其各自类别的影响。利用掩蔽图像建模等方法进行无监督训练，以克服标签不够精细以及避免负面的图像级注释影响，实现了对数据的更一般的统计结构的学习，并在四个流行的 Few-shot 分类基准测试中，对于 5-shot 和 1-shot 情形均取得了新的最优结果。