Abstract: Human-object interaction detection is to locate and identify the interactive relationship between humans and objects in an image. The challenge is that the machine cannot know which object the person is interacting in. Most existing methods try to solve this problem by matching humans and objects exactly. Different from them, this paper proposes an interactive instance proposal network based on relational reasoning to adapt to the task. Our main idea is to recommend human-object pairs by using the potential interaction relationships in the visual relationship between humans and objects. In addition, a cross-modal information fusion module is designed to fuse different context information according to its influence on the detection result, so as to improve the detection accuracy. To evaluate the proposed method, we performed sufficient experiments on two large-scale datasets: HICO-DET and V-COCO. Results show that our method achieves 19.90% and 50.3% mAP on HICO-DET and V-COCO, which are 4.5% and 2.8% higher than our baseline, respectively.