解读社会关系的双重视角模型
Dual-Glance Model for Deciphering Social Relationships
摘要：
自早期文明开始以来，源于每个人的社会关系基本上构成了我们日常生活中社会结构的基础。在计算机视觉文献中，场景理解已经取得了很大的进展，例如目标检测和场景解析。最近的研究集中在基于功能和几何关系的对象之间的关系。在这项工作中，我们的目标是研究社会关系识别的问题，在静态图像。我们提出了一个社会关系识别的双视线模型，其中第一视线固定在个人对的兴趣和第二视线部署注意力机制，以探索上下文线索。我们还收集了一个新的大规模社会背景中的人（PISC）数据集，其中包括来自9种社会关系的22，670张图像和76，568个注释样本。我们在PISC数据集上提供了基准测试结果，并定性地证明了所提出的模型的有效性。
英文摘要：
Since the beginning of early civilizations, social relationships derived from each individual fundamentally form the basis of social structure in our daily life. In the computer vision literature, much progress has been made in scene understanding, such as object detection and scene parsing. Recent research focuses on the relationship between objects based on its functionality and geometrical relations. In this work, we aim to study the problem of social relationship recognition, in still images. We have proposed a dual-glance model for social relationship recognition, where the first glance fixates at the individual pair of interest and the second glance deploys attention mechanism to explore contextual cues. We have also collected a new large scale People in Social Context (PISC) dataset, which comprises of 22,670 images and 76,568 annotated samples from 9 types of social relationship. We provide benchmark results on the PISC dataset, and qualitatively demonstrate the efficacy of the proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
