Human–object interaction detection based on disentangled axial attention transformer

Human–object interaction (HOI) detection aims to localize and infer interactions between human and objects in an image. Recent work proposed transformer encoder–decoder architectures for HOI detection with exceptional performance, but possess certain drawbacks: they do not employ a complete disentanglement strategy to learn more discriminative features for different sub-tasks; they cannot achieve sufficient contextual exchange within each branch, which is crucial for accurate relational reasoning; their transformer models suffer from high computational costs and large memory usage due to complex attention calculations. In this work, we propose a disentangled transformer network that disentangles both the encoder and decoder into three branches for human detection, object detection, and interaction classification. Then we propose a novel feature unify decoder to associate the predictions of each disentangled decoder, and introduce a multiplex relation embedding module and an attentive fusion module to perform sufficient contextual information exchange among branches. Additionally, to reduce the model’s computational cost, a position-sensitive axial attention is incorporated into the encoder, allowing our model to achieve a better accuracy-complexity trade-off. Extensive experiments are conducted on two public HOI benchmarks to demonstrate the effectiveness of our approach. The results indicate that our model outperforms other methods, achieving state-of-the-art performance.