论文标题
视觉关系检测的神经信息传递
Neural Message Passing for Visual Relationship Detection
论文作者
论文摘要
视觉关系检测旨在检测图像中对象之间的相互作用。但是，由于对象和相互作用的多样性，此任务遭受了组合爆炸的影响。由于与同一对象相关的相互作用是依赖的，因此我们探讨了相互作用的依赖性以减少搜索空间。我们通过交互图明确对对象和交互作用进行建模，然后提出一种消息式风格的算法来传播上下文信息。因此，我们称之为建议的方法神经信息传递（NMP）。我们进一步整合语言先验和空间提示，以排除不切实际的互动并捕获空间互动。两个基准数据集的实验结果证明了我们提出的方法的优越性。我们的代码可在https://github.com/phyllish/nmp上找到。
Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions. Since the interactions associated with the same object are dependent, we explore the dependency of interactions to reduce the search space. We explicitly model objects and interactions by an interaction graph and then propose a message-passing-style algorithm to propagate the contextual information. We thus call the proposed method neural message passing (NMP). We further integrate language priors and spatial cues to rule out unrealistic interactions and capture spatial interactions. Experimental results on two benchmark datasets demonstrate the superiority of our proposed method. Our code is available at https://github.com/PhyllisH/NMP.
