Leave-One-Out Cross-Validation for Tremor Classification and Rating Estimation

In this article, the authors propose a new method for transfer learning in pose estimation tasks, called Squeezing and Fusion of Short-range and Long-range Information (SF-SRLI). The main idea is to enhance the learning of relevant information by squeezing the channel outputs of neighboring nodes based on their distance from the target node. This allows the model to focus on the most important information, while filtering out noise.
The authors start by explaining that traditional methods for transfer learning in pose estimation tasks have limitations. These methods use a single shared weight for all joints, which can lead to poor performance when the number of samples is small. To overcome this limitation, they propose using Graph Neural Networks (GNNs) with a Spatial Attention mechanism. GNNs are effective in modeling relational data, such as human poses, and the spatial attention mechanism allows the model to focus on the most relevant information.
The authors then introduce the SF-SRLI method, which consists of two main components: Channel-Squeezing Block and Short-range Information Relevancy Fusion (SIRF) Block. The Channel-Squeezing Block squeezes the channel outputs of neighboring nodes based on their distance from the target node. This is done by defining hop-0 as the self node, hop-1 and 2 as short-range nodes, and hop-3 to hop-max as long-range nodes. The SIRF Block fuses the squeezed channel outputs with the target node’s feature map using a spatial attention mechanism.
The authors evaluate their method on two datasets: MPII Human Pose and LSP-Human Pose. They show that SF-SRLI outperforms traditional transfer learning methods in terms of accuracy and efficiency. They also perform ablation studies to demonstrate the effectiveness of each component of the proposed method.
In summary, the authors propose a new method for transfer learning in pose estimation tasks called SF-SRLI, which squeezes and fuses short-range and long-range information using Graph Neural Networks with a Spatial Attention mechanism. The method outperforms traditional methods in terms of accuracy and efficiency, and demonstrates the effectiveness of each component through ablation studies.