Calibrating Biased Distribution in VFM-Derived Latent Space via Cross-Domain Geometric Consistency.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Deep learning models face challenges due to the discrepancy between observed training samples and true data distributions, caused by factors like sampling bias and noise.
• Foundation models offer powerful feature extraction capabilities, but their application in addressing distribution gaps requires further investigation.

Purpose of the Study:

• To introduce a geometric knowledge-guided distribution calibration framework to address data distribution discrepancies.
• To demonstrate the transferability of geometric feature distribution shapes across domains and datasets using foundation models.
• To validate the framework's effectiveness in challenging settings like federated learning and long-tailed recognition.

Main Methods:

• Leveraging off-the-shelf vision foundation models (e.g., CLIP, DINOv2) for feature extraction to capture geometric distribution shapes.
• Developing a privacy-preserving technique to acquire global geometric shapes in federated learning for sample generation.
• Utilizing transferred geometric knowledge from data-rich categories to reconstruct distributions for data-scarce classes in long-tailed recognition.

Main Results:

• The geometric shapes of feature distributions extracted by foundation models show remarkable transferability across diverse domains and datasets.
• The proposed framework effectively bridges the gap between local and global observations in federated learning by generating new client samples.
• Geometric knowledge transfer significantly improves the recovery of true distributions for sample-scarce tail classes in long-tailed recognition.
• Comprehensive experiments confirm boosted performance across benchmarks in both federated learning and long-tailed recognition settings.

Conclusions:

• Geometric knowledge-guided distribution calibration is a viable strategy to overcome information deficits caused by data heterogeneity and sample imbalance.
• The transferability of geometric feature distribution properties from foundation models offers a promising direction for improving deep learning model robustness and performance.
• The framework demonstrates practical utility in addressing critical challenges in federated learning and long-tailed recognition, enhancing model generalization and fairness.