Abstract
In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to group object labels with similar patterns of relationship distribution in the dataset into fewer categories. Label clustering not only mitigates both the large classification space and class imbalance issues, but also potentially increases data samples for each clustered category. We further propose to incorporate depth information as an additional feature into the instance segmentation model. The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver. We have rigorously evaluated the proposed techniques and performed various ablation analysis to validate the benefits of them.
Abstract (translated)
在本文中，我们研究了无监督标签聚类技术的使用，并证明它可以显着改善上下文（PIC）数据集中的视觉关系预测准确性。我们建议将数据集中具有相似关系分布模式的对象标签分组为更少的类别。标签聚类不仅可以减轻大的分类空间和类不平衡问题，还可以增加每个聚类类别的数据样本。我们进一步建议将深度信息作为附加特征结合到实例分割模型中。附加深度预测路径以边界框或分割掩模不能传递的方式补充关系预测模型。我们严格评估了所提出的技术，并进行了各种消融分析，以验证它们的好处。
URL
https://arxiv.org/abs/1809.02945
