Multi-Task Keypoint Prediction Using Error-Distribution Loss
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Solution Overview
Problem
Existing multi-task learning methods, particularly in key point prediction tasks, suffer from ineffective training due to inappropriate loss construction, leading to joint training failures and reduced accuracy.
Innovation Solution
Construct a loss function based on the real error distribution between the prediction result and the key point position label, using a flow model to determine the error distribution and apply residual likelihood estimation to optimize the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing loss function is used for key point prediction task, then training process is simple, but training effect is poor and joint training fails
Solution Approach 1:
The patent changes the loss function parameters from standard L1/L2 norms to a custom loss that incorporates error distribution modeling. The loss function is modified to include a weighting factor that adjusts based on the relationship between prediction errors and key point positions, thereby improving training reliability while managing complexity through parameterization.
Solution Approach 2:
The patent implements a feedback mechanism where the loss function continuously adjusts based on the distribution of prediction errors. By monitoring the error distribution and adapting the loss weighting accordingly, the system improves training effectiveness while maintaining manageable complexity through iterative optimization.
2Reliability
If multiple separate models are used for different prediction tasks, then each task can be optimized independently, but the number of models increases and computational resources are consumed
Solution Approach 1:
The patent merges multiple prediction tasks into a single unified model that processes all tasks simultaneously. By combining key point prediction with other prediction tasks in one model architecture, the system reduces the total number of models while maintaining independent optimization capabilities for each task through separate loss function components.
Solution Approach 2:
The patent creates a universal model that can perform multiple prediction tasks. The single model is designed with multi-functional capabilities, using shared feature extraction layers and task-specific output heads, allowing it to accurately perform key point prediction alongside other prediction tasks without requiring separate specialized models.
Data Source
AI summary
The embodiment of the disclosure discloses a method of multi-task prediction and device, electronic equipment and a storage medium, and the method comprises the steps: inputting an original image into a preset model; outputting a prediction result of at least one prediction task for the original image through the preset model, wherein the at least one prediction task comprises a key point prediction task; the loss item of the preset model in the training process comprises a first loss constructed according to the error distribution between the first prediction result of the key point prediction task and the key point position label.