Human-object interaction (HOI) detection as a downstream of object detection tasks requires localizing pairs of humans and objects and extracting the semantic relationships between humans and objects from an image. Recently, one-stage approaches have become a new trend for this task due to their high efficiency. However, these approaches focus on detecting possible interaction points or filtering human-object pairs, ignoring the variability in the location and size of different objects at spatial scales. To address this problem, we propose a transformer-based method, QAHOI (Query-Based Anchors for Human-Object Interaction detection), which leverages a multi-scale architecture to extract features from different spatial scales and uses query-based anchors to predict all the elements of an HOI instance. We further investigate that a powerful backbone significantly increases accuracy for QAHOI, and QAHOI with a transformer-based backbone outperforms recent state-of-the-art methods by large margins on the HICO-DET benchmark. The source code is available at $\href{https://github.com/cjw2021/QAHOI}{\text{this https URL}}$.
翻译:人类物体相互作用(HOI)检测作为物体探测任务下游的物体探测任务,需要将人类和物体的对子定位,并从图像中提取人与物体之间的语义关系。最近,由于效率高,一阶段方法已成为这项任务的新趋势。然而,这些方法侧重于探测可能的相互作用点或过滤人体物体对子,忽视空间尺度上不同物体的位置和大小的变异性。为了解决这一问题,我们提议采用变压器法,QAHOI(人类物体相互作用探测的以查询为基础的锚),利用多尺度结构从不同的空间尺度上提取特征,并使用基于查询的锚来预测HOI实例的所有元素。我们进一步调查,强大的脊椎大大提高了QAHOHOI和具有以变压器为主脊的QAHOI的精度,在HICO-DET基准上以大边距显示最近的状态-艺术方法。源代码可在 $href$_hus_QHOImbU. /QULAchus_Qrus.commexus.