Abstract: Human-object interaction(HOI) detection aims to simultaneously identify humans,interacting objects,and their corresponding interaction relationships in images,and constitutes a fundamental task in behavior understanding and high-level visual semantic analysis.To address the challenges of effectively modeling long-range,cross-region human-object interactions in complex scenes,as well as the high computational cost of existing methods,an efficient HOI detection framework is proposed,which reduces model complexity while maintaining high detection accuracy.The framework introduces a Mamba Encoder to model image feature sequences,enabling efficient integration of global contextual information and thereby enhancing the capability to capture long-range human-object interaction relationships.On this basis,a keypoint selection mechanism is designed to guide the model to focus on discriminative regions that are highly relevant to interaction semantics.Combined with deformable attention,the selected key features are further refined to improve interaction recognition performance under complex scene conditions.In addition,a human-object interaction pair representation that explicitly incorporates spatial relationships is constructed.By introducing spatial relation encoding and performing joint feature fusion,the robustness and discriminability of interaction semantic representations are further enhanced.Experimental results on the HICO-DET and V-COCO benchmark datasets demonstrate that the proposed approach achieves a favorable balance between detection accuracy and computational efficiency.Compared with existing methods,it exhibits clear performance advantages across multiple evaluation metrics while maintaining lower parameter counts and computational complexity.Both qualitative and quantitative analyses further validate the effectiveness of the proposed approach in mo-deling long-range dependencies,adapting to complex scenes,and enabling efficient inference.

Key words: Human-object interaction detection, Long-range dependency modeling, Mamba Encoder, Deformable attention, Spatial relationship modeling