Keypoint Prediction Training With Heatmap-Regression Fusion

The patent combines heatmap-based methods and regression-based methods into a unified keypoint detection framework. The model simultaneously learns heatmap representation and regression prediction, allowing the system to benefit from both approaches: the heatmap provides accurate keypoint location guidance while the regression component ensures computational efficiency. This merging resolves the contradiction by integrating the strengths of both methods.

The patent employs a composite loss function that integrates multiple loss components (heatmap loss, regression loss, and consistency loss) to train the keypoint detection model. This composite approach combines the accuracy benefits of heatmap-based methods with the speed advantages of regression-based methods, creating a hybrid solution that achieves both high accuracy and computational efficiency.

The patent merges heatmap-based methods and regression-based methods into a unified framework where the model simultaneously performs heatmap regression and coordinate prediction. This integration allows the system to achieve the accuracy of heatmap methods while maintaining the computational efficiency of regression methods, resolving the speed-accuracy tradeoff.

The patent segments the keypoint detection task into two complementary components: heatmap generation for accurate location estimation and regression prediction for efficient coordinate output. By dividing the detection process into these two segments that work together, the system achieves both high accuracy and computational speed.