Abstract: Fine-grained information,as a kind of contextual information,can assist models in recognizing human-object interactions with similar relative spatial relationships.However,how to utilize this key cue to uniformly model feature information of different granularities on multi-scale feature maps remains a critical challenge that hinder further improvement of human-object interaction detection accuracy.To address this problem,this paper proposes a human-object interaction detection model based on fine-grained attention mechanism.The model strengthens local features under the guidance of fine-grained information.It fuses feature maps of different scales and automatically learns image content through a deformable attention mechanism.Additionally,it models the long-range dependencies between features of various granularities,essentially improving the accuracy of the human-object interaction detection model.Extensive experiments are conducted on the V-COCO and HICO datasets.The experimental results show that the proposed method has increased the mAPby 7.7 percentage points on the V-COCO dataset,and the mAP has increased by 7.43,7.5 and 7.85 percentage points on the HICO dataset compared to the baseline models.

Key words: Deep learning, Human-Object interaction detection, Fine-grained information, Attention mechanism