论文标题
用于检测人类对象相互作用的空间条件图
Spatially Conditioned Graphs for Detecting Human-Object Interactions
论文作者
论文摘要
我们解决了使用图形神经网络在图像中检测人类对象相互作用的问题。与传统的方法不同，节点向每个邻居发送缩放但其他相同的消息，我们建议在其空间关系上调节消息之间的消息，从而导致不同的消息转移给同一节点的邻居。为此，我们探讨了在多分支结构下应用空间调节的各种方法。通过广泛的实验，我们证明了空间调节的优势，用于计算邻接结构，消息和精制图形特征。特别是，我们从经验上表明，随着边界框的质量提高，与空间信息相比，它们的粗外观特征对相互作用的歧义差异相对较小。我们的方法在HICO-DET上获得了31.33％的地图，在V-Coco上获得了54.2％的地图，在微调检测方面的表现明显优于最先进的。
We address the problem of detecting human-object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in different messages going to neighbours of the same node. To this end, we explore various ways of applying spatial conditioning under a multi-branch structure. Through extensive experimentation we demonstrate the advantages of spatial conditioning for the computation of the adjacency structure, messages and the refined graph features. In particular, we empirically show that as the quality of the bounding boxes increases, their coarse appearance features contribute relatively less to the disambiguation of interactions compared to the spatial information. Our method achieves an mAP of 31.33% on HICO-DET and 54.2% on V-COCO, significantly outperforming state-of-the-art on fine-tuned detections.
