论文标题
注意力流：端到端关注的关注估计
Attention Flow: End-to-End Joint Attention Estimation
论文作者
论文摘要
本文解决了在第三人称社交场景视频中了解共同关注的问题。共同关注是两个或多个人在一个物体或感兴趣领域的共同注视行为，并且具有广泛的应用，例如人类计算机的互动，教育评估，注意力障碍患者的治疗等等。我们的方法（注意力流）通过使用显着性注意图和两个新型的卷积注意机制来以端到端方式学习共同的关注，这些卷积注意机制决定选择相关特征并改善联合注意力的本地化。我们比较了显着图和注意力机制的影响，并报告了定量和定性结果对录像带数据集中的共同注意的检测和定位，该数据集包含复杂的社交场景。
This paper addresses the problem of understanding joint attention in third-person social scene videos. Joint attention is the shared gaze behaviour of two or more individuals on an object or an area of interest and has a wide range of applications such as human-computer interaction, educational assessment, treatment of patients with attention disorders, and many more. Our method, Attention Flow, learns joint attention in an end-to-end fashion by using saliency-augmented attention maps and two novel convolutional attention mechanisms that determine to select relevant features and improve joint attention localization. We compare the effect of saliency maps and attention mechanisms and report quantitative and qualitative results on the detection and localization of joint attention in the VideoCoAtt dataset, which contains complex social scenes.
