提出一种基于多数标签的多分类多实例学习新问题，提升模型对个体实例的分类能力。
Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning
- 通过计数网络统计每类实例数量，生成袋级多数标签
- 在4个数据集上优于传统MIL方法，多数类占比越高效果越佳
- 适合病理图像、情感分析等标签不精确场景，尤其关注多数类建模
本文提出一种新型多分类多实例学习（MIL）问题——从多数标签中学习（LML）。在该问题中，一个袋子的袋级标签由其中实例的多数类别决定。目标是训练模型准确预测每个实例的类别。该问题在病理图像分割、政治投票预测、客户情感分析和环境监测中具有重要应用价值。为此，我们提出计数网络，通过统计各类别实例数量来估计袋级多数标签。分析实验发现，多数类比例高的袋子更有利于学习。基于此，设计了多数比例增强模块（MPEM），通过移除袋内少数类实例来提高多数类占比。实验表明，所提方法在四个数据集上均优于传统MIL方法；消融实验验证了各模块的有效性。代码已公开于GitHub。
原文摘要 · Abstract (English)
The paper proposes a novel multi-class Multiple-Instance Learning (MIL) problem called Learning from Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag-level label. The goal of LML is to train a classification model that estimates the class of each instance using the majority label. This problem is valuable in a variety of applications, including pathology image segmentation, political voting prediction, customer sentiment analysis, and environmental monitoring. To solve LML, we propose a Counting Network trained to produce bag-level majority labels, estimated by counting the number of instances in each class. Furthermore, analysis experiments on the characteristics of LML revealed that bags with a high proportion of the majority class facilitate learning. Based on this result, we developed a Majority Proportion Enhancement Module (MPEM) that increases the proportion of the majority class by removing minority class instances within the bags. Experiments demonstrate the superiority of the proposed method on four datasets compared to conventional MIL methods. Moreover, ablation studies confirmed the effectiveness of each module. The code is available at \href{https://github.com/Shiku-Kaito/Learning-from-Majority-Label-A-Novel-Problem-in-Multi-class-Multiple-Instance-Learning}{here}.
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