Weakly Supervised Multilabel Clustering and its Applications in Computer Vision
提出弱监督多标签聚类方法，允许一个包有多个标签，利用包级标签指导实例级聚类，通过弱监督随机森林和确定性退火优化，在图像聚类、语义分割和多目标定位任务上表现优异。
Clustering is a useful statistical tool in computer vision and machine learning. It is generally accepted that introducing supervised information brings remarkable performance improvement to clustering. However, assigning accurate labels is expensive when the amount of training data is huge. Existing supervised clustering methods handle this problem by transferring the bag-level labels into the instance-level descriptors. However, the assumption that each bag has a single label limits the application scope seriously. In this paper, we propose weakly supervised multilabel clustering, which allows assigning multiple labels to a bag. Based on this, the instance-level descriptors can be clustered with the guidance of bag-level labels. The key technique is a weakly supervised random forest that infers the model parameters. Thereby, a deterministic annealing strategy is developed to optimize the nonconvex objective function. The proposed algorithm is efficient in both the training and the testing stages. We apply it to three popular computer vision tasks: 1) image clustering; 2) semantic image segmentation; and 3) multiple objects localization. Impressive performance on the state-of-the-art image data sets is achieved in our experiments.