# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 2 Related Work  
With the rapid advancement of deep learning technology, relevant research in the field of pose estimation has been actively conducted, which has continuously improved its accuracy and efficiency. This section explains the main features of models that have achieved state-of-the-art (SOTA) pose estimation and reviews representative deep learning-based lightweight pose estimation models. The stacked hourglass network [2] is a representative deep-learning network model used for HPE. This model can progressively extract high-resolution and low-resolution features from input images through repeated down- and up-sampling processes. Each hourglass module analyzes the image by compressing features to progressively smaller resolutions and restoring them to the original size to recover detailed positional information. During this process, skip connections are used between the encoder and decoder to transfer precise information extracted by the encoder to the decoder, minimizing information loss and alleviating the vanishing gradient problem that is common in deep networks. The model's structure indicates that the stacked hourglass network achieves accurate and consistent estimation results, especially for complex human poses. The soft-gated skip connection [8] is a key component of the stacked hourglass network that improves the residual block [9]. The authors previously identified that modifying the residual block used within the encoder and decoder of the stacked hourglass network causes training limitations, and they introduced the soft gating mechanism to address this challenge. They used gated skip connections with learnable parameters to determine which channels in the feature map from the previous stage should be weighted more through backpropagation, ensuring the optimal information was conveyed. This stacked hourglass network consists of multiple stacked modules, resulting in high computational costs and increased model size, which may be impractical in real-time or resource-constrained environments. The cascade pyramid network (CPN) [10] proposes a model consisting of GlobalNet and RefineNet to effectively predict occluded or overlapping keypoints. GlobalNet captures the global context and performs approximate pose estimation by extracting heatmaps from feature maps of various resolutions in a pyramid structure. RefineNet generates more precise keypoints using higher-resolution feature maps based on the initial heatmaps extracted by GlobalNet. Additionally, as training progresses, the model tends to focus on simpler keypoints while ignoring more challenging ones. Hard keypoints are selected for training through online hard keypoint mining to effectively learn both types. However, this multi-stage refinement process increases computational overhead and complicates training, making the model less suitable for resource-limited environments. [PERSON] et al. [11] proposed a method that simplified the existing complex structure. After processing the input image using a ResNet [9]-based backbone network, deconvolution [12] layers were added to restore the resolution and generate high-resolution keypoint heatmaps. This approach effectively simplifies pose estimation tasks and maintains accuracy; however, it still requires significant computational resources owing to the reliance on ResNet-based backbones and deconvolution layers. OpenPose [13] uses a VGG-19-based [14] backbone to extract feature maps and generates heatmaps representing keypoint probabilities and part affinity fields representing the connectivity between keypoints through initial and refinement stages, performing multi-person pose estimation. LightWeight OpenPose [15] is a lightweight model based on OpenPose that uses MobileNetV1 [16] as the backbone to reduce computational costs and memory usage. Additionally, most computational processes for deriving heatmaps and part affinity fields in the refinement stage are shared.