MAPoseNet: Animal pose estimation network via multi-scale convolutional attention

Abstract

Animal pose estimation serves as an upstream task for recognizing and understanding animal behavior. Over the last year, the accuracy of the deep learning-based method has steadily improved, but at the expense of the model’s inference speed. This paper uses an efficient and powerful model to improve inference speed and accuracy. The classic encoder–decoder architecture is chosen. For estimating animal pose, our model based on a feature pyramid and a multi-scale asymmetric convolution attention mechanism is developed and named MAPoseNet (Animal Pose Estimation Network Via Multi-scale Convolutional Attention). MAPoseNet consists of an encoder and a decoder. Rather than typical self-attention, the encoder’s attention mechanism comprises multi-scale, asymmetric convolutions that are lightweight and instrumental in improving inference speed. A feature pyramid and a feature balance module make up the decoder. The public dataset AP-10K is used to train and test MAPoseNet. A series of experimental results demonstrate that the MAPoseNet model provides cutting-edge performance. MAPoseNet outperforms HRFormer by 1.3 AP and 0.8 AR, with 33.7% fewer FLOPs and 66% faster inference speed. And our model surpasses HRNet and HRFormer on the Animal Pose dataset as well. Our model has achieved a win-win situation regarding inference speed and accuracy.