论文标题
从现场学习并从富人那里借钱：在场景图中解决长尾
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
论文作者
论文摘要
尽管近年来场景图的产生取得了巨大进展，但其对象关系中的长尾分布仍然是一个具有挑战性和缠扰的问题。现有方法在很大程度上依赖外部知识或统计偏见信息来减轻此问题。在本文中，我们从其他两个方面解决了这个问题：（1）旨在通过添加注意机制从场景中学习特定知识的场景 - 对象相互作用； （2）试图将从头部学到的丰富知识转移到尾巴上的长尾知识转移。在基准数据集的三个任务上进行的大量实验表明，我们的方法的表现优于当前的最新竞争者。
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors.
