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PnP-GA+: Plug-and-Play Domain Adaptation for Gaze Estimation Using Model Variants
IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 1, 2024
Summary
This study introduces PnP-GA+, an enhanced method for appearance-based gaze estimation. It improves model adaptability to new domains by assembling diverse model variants, boosting performance in unseen environments.
Area of Science:
- Computer Vision
- Machine Learning
Background:
- Appearance-based gaze estimation models struggle with performance in new domains.
- Existing methods lack adaptability to unseen environments or individuals.
Purpose of the Study:
- To enhance the domain adaptability of gaze estimation models.
- To introduce PnP-GA+, an extended plug-and-play method for gaze domain adaptation.
Main Methods:
- PnP-GA+ assembles model variants considering color space, data augmentation, and model structure.
- An intra-group attention module dynamically optimizes pseudo-labeling during adaptation.
- The framework integrates existing gaze estimation networks for cross-domain enhancement.
Main Results:
- PnP-GA+ outperforms state-of-the-art domain adaptation approaches on four standard gaze domain adaptation tasks.
- The method consistently enhances cross-domain performance.
- Versatility is improved through diverse model group assembly.
Conclusions:
- PnP-GA+ offers a versatile and effective solution for improving gaze estimation model performance across diverse domains.
- The approach demonstrates significant advancements in addressing the challenge of domain shift in gaze estimation.
