Expression Recognition With Uncertainty-Guided Active Learning
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Solution Overview
Problem
Existing expression recognition methods struggle with large-scale data and complex scenes, requiring laborious and expensive labeling and are sensitive to distribution and quality of training data, and existing methods fail to effectively combine uncertainty with the performance of the model, and existing methods fail to effectively address the challenges of noise labels or uneven data distribution, and existing methods do not effectively address the challenges of noise labels or uneven data distribution, and existing methods fail to effectively address the challenges of noise labels or uneven data distribution.
Innovation Solution
A method is proposed that integrates uncertainty estimation and active learning, which combines the use of a self-attention weight module, and the use of a logarithmic weighted cross-entropy loss, and the use of a dynamic threshold re-labeling module to enhance the recognition of the model, and the use of a self-attention weight module to enhance the recognition of the model, and the use of a logarithmic weighted cross-entropy loss, and the use of a dynamic threshold re-labeling module to enhance the recognition of the model, and the use of a logarithmic weighted cross-entropy loss, and the use of a dynamic threshold re-labeling method to enhance the recognition of the model, and the use of a self-attention weight module to enhance the recognition of the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for expression recognition, then recognition accuracy is improved, but labeling cost and time increase significantly
Solution Approach 1:
The system performs self-labeling through uncertainty estimation and active learning, where the model identifies its own uncertain predictions and selectively queries labels only for those samples. This self-service mechanism reduces dependency on manual labeling while maintaining high accuracy.
Solution Approach 2:
The system implements a feedback loop where uncertainty estimates from the model guide the selection of samples for labeling, and the newly labeled samples are used to retrain and improve the model. This iterative feedback process continuously enhances recognition accuracy while minimizing labeling effort.
2Measurement precision
If more training data is used to improve model performance, then recognition accuracy increases, but labeling cost increases
Solution Approach 1:
The system extracts only the most informative samples based on uncertainty estimation, rather than labeling all available data. By taking out and focusing on the critical subset of samples that contribute most to model improvement, labeling cost is significantly reduced while maintaining performance gains.
Solution Approach 2:
The system changes the parameter of sample selection from uniform random sampling to uncertainty-based sampling. This parameter change allows the system to prioritize samples with higher uncertainty, achieving better performance with fewer labeled samples.
3Productivity
If the model is trained on noisy or unevenly distributed data, then training speed increases, but model reliability deteriorates
Solution Approach 1:
The system converts the harmful effect of noisy and uneven data into a benefit by using uncertainty estimation to identify and prioritize samples that are most useful for training. Samples with high uncertainty often contain valuable information that, when properly labeled, significantly improve model robustness against noise and data imbalance.
Solution Approach 2:
The system performs preliminary uncertainty estimation before full training to identify problematic samples. This preliminary action allows the system to focus labeling efforts on samples that will most improve reliability, preventing the model from learning incorrect patterns from noisy data.
Data Source
AI summary
The disclosure relates to the technical fields of deep learning and image processing, and provides an expression recognition method incorporated with uncertainty estimation and active learning. The expression recognition method incorporated with uncertainty estimation and active learning includes following steps: 1. contrastive self-supervised pre-training; 2. a Dynamic Re-labeling Module (DRM) incorporated with uncertainty estimation; and 3. model adjustment based on a pre-training model. In the expression recognition method incorporated with uncertainty estimation and active learning according to the disclosure, firstly, feature extraction ability of the model is improved using contrastive learning based on low-level feature representations of the whole unlabeled data set and with the improved uncertainty estimation method. Subsequently, the model is fine-tuned through the active learning based on the pre-training model, so as to select and label most informative samples more efficiently. Compared with related art, recognition accuracy is significantly improved.