To enhance JCRA's performance, particularly for medium-sized targets, several strategies can be implemented:
Refinement Mechanisms: Introducing refinement mechanisms in the keypoint decoder can help improve the accuracy of keypoint predictions, especially for medium-sized targets. This can involve incorporating additional layers or modules that focus on refining the localization of keypoints based on contextual information.
Data Augmentation: Augmenting the training data with more examples of medium-sized targets can help the model learn to better localize keypoints in such scenarios. This can include augmenting the dataset with various scales, poses, and occlusions to make the model more robust.
Loss Function Optimization: Fine-tuning the loss function to give more weight to medium-sized targets during training can guide the model to focus on improving accuracy specifically for these instances. Adjusting the loss function to address the challenges specific to medium-sized targets can lead to better performance.
Architectural Adjustments: Making architectural adjustments, such as increasing the depth or width of certain layers in the network that are crucial for handling medium-sized targets, can help in capturing more intricate details and nuances in the pose estimation process.
Ensemble Methods: Implementing ensemble methods by combining multiple JCRA models trained with different initializations or hyperparameters can help in capturing diverse patterns and improving overall performance, especially for medium-sized targets.