IEEE Trans Pattern Anal Mach Intell. 2017 Sep;39(9):1839-1852. doi: 10.1109/TPAMI.2016.2613865. Epub 2016 Sep 27.
We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential functions. Consequently, it can be used to encode different cardinality-based multi-instance assumptions, ranging from the standard MIL assumption to more general assumptions. In addition, this framework can be efficiently used for both binary and multiclass classification. To this end, an efficient inference algorithm and a discriminative latent max-margin learning algorithm are introduced to train and test the proposed multi-instance Markov network models. We evaluate the performance of the proposed framework on binary and multi-class MIL benchmark datasets as well as two challenging computer vision tasks: cyclist helmet recognition and human group activity recognition. Experimental results verify that encoding the degree of ambiguity in data can improve classification performance.
我们提出了一种基于马尔可夫网络的多示例学习（MIL）概率图形框架。该框架可以通过参数化基数势函数来处理弱监督数据中不同程度的标记模糊性（即袋中正实例的部分）。因此，它可用于编码基于基数的不同多示例假设，范围从标准的 MIL 假设到更一般的假设。此外，该框架可有效地用于二进制和多类分类。为此，引入了一种有效的推理算法和一种判别潜在最大间隔学习算法，以训练和测试所提出的多示例马尔可夫网络模型。我们在二进制和多类 MIL 基准数据集以及两个具有挑战性的计算机视觉任务（自行车手头盔识别和人群活动识别）上评估了所提出框架的性能。实验结果验证了对数据中模糊度的编码可以提高分类性能。
