Animal pose estimation has important value in both theoretical research and practical applications, such as zoology and wildlife conservation. A simple but effective high-resolution Transformer model for animal pose estimation called DepthFormer is provided in this study to address the issue of large-scale models for multi-animal pose estimation being problematic with limited computing resources. We make good use of a multi-branch parallel design that can maintain high-resolution representations throughout the process. Along with two similarities, i.e., sparse connectivity and weight sharing between self-attention and depthwise convolution, we utilize the delicate structure of the Transformer and representative batch normalization to design a new basic block for reducing the number of parameters and the amount of computation required. In addition, four PoolFormer blocks are introduced after the parallel network to maintain good performance. Benchmark evaluation is performed on a public database named AP-10K, which contains 23 animal families and 54 species, and the results are compared with the other six state-of-the-art pose estimation networks. The results demonstrate that the performance of DepthFormer surpasses that of other popular lightweight networks (e.g., Lite-HRNet and HRFormer-Tiny) when performing this task. This work can provide effective technical support to accurately estimate animal poses with limited computing resources.
Macaque monkey is a rare substitute which plays an important role for human beings in relation to psychological and spiritual science research. It is essential for these studies to accurately estimate the pose information of macaque monkeys. Many large-scale models have achieved state-of-the-art results in pose macaque estimation. However, it is difficult to deploy when computing resources are limited. Combining the structure of high-resolution network and the design principle of light-weight network, we propose the attention-refined light-weight high-resolution network for macaque monkey pose estimation (HR-MPE). The multi-branch parallel structure is adopted to maintain high-resolution representation throughout the process. Moreover, a novel basic block is designed by a powerful transformer structure and polarized self-attention, where there is a simple structure and fewer parameters. Two attention refined blocks are added at the end of the parallel structure, which are composed of light-weight asymmetric convolutions and a triplet attention with almost no parameter, obtaining richer representation information. An unbiased data processing method is also utilized to obtain an accurate flipping result. The experiment is conducted on a macaque dataset containing more than 13,000 pictures. Our network has reached a 77.0 AP score, surpassing HRFormer with fewer parameters by 1.8 AP.
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