The human-object interaction (HOI) detection task refers to localizing humans, localizing objects, and predicting the interactions between each human-object pair. HOI is considered one of the fundamental steps in truly understanding complex visual scenes. For detecting HOI, it is important to utilize relative spatial configurations and object semantics to find salient spatial regions of images that highlight the interactions between human object pairs. This issue is addressed by the proposed self-attention based guided transformer network, GTNet. GTNet encodes this spatial contextual information in human and object visual features via self-attention while achieving a 4%-6% improvement over previous state of the art results on both the V-COCO and HICO-DET datasets. Code will be made available online.
翻译:人体- 物体互动( HOI) 检测任务是指将人类本地化、物体本地化和预测每个人体- 对象对应方之间的相互作用。 HOI 被视为真正理解复杂视觉场景的基本步骤之一。 为了检测 HOI, 使用相对空间配置和物体语义来寻找突出显示人类对象对子之间相互作用的图像的显著空间区域非常重要。 这个问题由拟议的基于自我注意的引导变压器网络GTNet 来解决。 GTNet 通过自我注意将人类和物体视觉特征的空间背景信息编码为人类和物体视觉特征,同时比 V- COCO 和 HICO- DET 数据集以往的艺术成果提高4%-6%。 代码将在线提供。