The complementary augmentation strategy in PERE may have limitations in handling more complex scenarios where the unreliable boxes are not easily replaced or removed. One potential limitation is the reliance on high-confidence boxes for replacement, which may not always be available, especially in challenging scenarios with high uncertainty.
To improve the strategy and address these limitations, several enhancements can be considered:
Dynamic Thresholding: Implementing dynamic thresholding techniques to adaptively adjust the confidence threshold based on the difficulty of the samples can help in better identifying unreliable boxes.
Uncertainty Estimation: Incorporating uncertainty estimation methods can provide a measure of confidence in the pseudo labels, allowing for more informed decisions on box replacement or removal.
Ensemble Approaches: Utilizing ensemble approaches to combine predictions from multiple models can help in making more reliable decisions on pseudo label refinement.
Active Learning: Introducing active learning strategies to selectively query uncertain samples for human annotation can improve the quality of pseudo labels in challenging scenarios.
By integrating these enhancements, the complementary augmentation strategy can be further improved to handle more complex scenarios and enhance the reliability of pseudo labels in unsupervised domain adaptation settings.