论文标题
单标签注释中的基于补丁的架构，用于多标签分类
A patch-based architecture for multi-label classification from single label annotations
论文作者
论文摘要
在本文中，我们为多标签分类问题提出了一个基于补丁的体系结构，其中仅在数据集图像中观察到一个正面标签。我们的贡献是双重的。首先，我们根据注意机制介绍了一个轻斑架构。接下来，利用嵌入自相似性的补丁，我们提供了一种新颖的策略来估计负面示例并处理积极和未标记的学习问题。实验表明，我们的体系结构可以从头开始训练，而对文献相关方法则需要在类似数据库上进行预训练。
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset. Our contributions are twofold. First, we introduce a light patch architecture based on the attention mechanism. Next, leveraging on patch embedding self-similarities, we provide a novel strategy for estimating negative examples and deal with positive and unlabeled learning problems. Experiments demonstrate that our architecture can be trained from scratch, whereas pre-training on similar databases is required for related methods from the literature.
