Deep Metric Learning With Adaptively Composite Dynamic Constraints.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Existing deep metric learning methods often use fixed constraints, limiting adaptability during training.
• Optimal constraint selection is crucial for achieving good generalization in metric learning.

Purpose of the Study:

• To propose a deep metric learning with adaptively composite dynamic constraints (DML-DC) method.
• To enhance image retrieval and clustering performance through adaptive constraint generation.

Main Methods:

• A learnable constraint generator adaptively produces dynamic constraints.
• The CSCW (Collection, Sampling, Construction, Weighting) paradigm is utilized.
• Cross-attention and graph neural networks are employed for sample-proxy relation modeling and constraint generation via meta-learning.

Main Results:

• The DML-DC method demonstrates effectiveness across five benchmarks.
• Adaptive constraints lead to improved generalization compared to pre-defined constraints.
• The meta-learning framework enables dynamic adaptation of constraints.

Conclusions:

• The proposed DML-DC method offers a flexible and effective approach to deep metric learning.
• Adaptive dynamic constraints are superior to static ones for metric learning tasks.
• The framework shows significant potential for image retrieval and clustering applications.