# An Axial Compression Transformer for Efficient Human Pose Estimation
## 1 Introduction
The basic principle is as follows:  
1. Text is human-generated, highly condensed information, while images are natural information containing a lot of redundancy and noise. Compression operations can reduce the useless information in images. 2. For human pose estimation tasks, compression operations do not significantly lose global semantic information. CNNs initially extract the local features from images, while Transformer structures tend to establish global dependencies. 3. From a biological perspective, human vision can partially fill in missing parts of an image. Similarly, the performance loss caused by missing some visual information in Transformers is relatively low. Compared to other lightweight methods, such as pruning [24; 25], upsampling [26], and depthwise convolution [27], our axial compression module significantly reduces the computational cost, while retaining keypoint global feature information. The experimental results demonstrate that this method effectively decreases the model parameter count and the computational cost, while maintaining high detection accuracy, providing a novel solution for lightweight human pose estimation tasks. Building upon this idea, we propose a new and efficient attitude estimation method based on axial compression Transformer, named ACPose. Figure 1 illustrates the ACPose architecture, we utilizes TokenPose [23] as the baseline network. It first extracts feature maps using CNNs, flattens them into visual tokens, and adds randomly initialized keypoint tokens to represent human keypoints. These tokens are then fed into Transformer. Subsequently, the input Transformer's feature matrix undergoes horizontal and vertical average pooling. Multi-head attention is applied to the compressed matrices, and the results are fused. The difference matrix obtained by subtracting the fused matrix from the original Transformer input is average-pooled and subjected to self-attention. This result is then merged with the output of the axial compression stage. Finally, we use the traditional heatmap methods to predict the keypoints. In summary, this article has the following three contributions:  
* Novel Compress-Merge Strategy: This paper introduces a novel compress-merge strategy, implemented in the proposed axial compression pose Transformer (ACPose) network for efficient human pose estimation. By employing compression operations to reduce the computational cost, while utilizing feature fusion to maintain Transformer's global receptive field, this strategy mitigates performance degradation caused by irrelevant information in human pose estimation tasks. * Local Enhancement Method: This work proposes a local enhancement method that leverages the difference between the compressed and original inputs to amplify the lost local details. This technique effectively enhances the extraction of local features, leading to a significant improvement in prediction accuracy. * Competitive Performance with Reduced Complexity: Extensive experiments conducted on prominent public datasets demonstrate that our approach achieves a substantial 81.2% reduction in computational cost of the Transformer part, while maintaining competitive accuracy. These results highlight the effectiveness of our proposed method in achieving lightweight, yet high-performing human pose estimation.