Discriminative Self-Paced Group-Metric Adaptation for Online Visual Identification

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Instance-level visual identification models struggle with distribution shifts between offline training and online testing data.
• Existing methods often fail to adapt effectively to unseen data due to these distribution discrepancies.

Purpose of the Study:

• To propose a novel online group-metric adaptation model to enhance visual identification performance on unseen online data.
• To address the limitations of existing methods in handling distribution shifts.

Main Methods:

• Developed an online group-metric adaptation model utilizing a frequent sharing-subset mining module to identify groups of visually similar testing samples.
• Introduced self-paced learning (SPL) to gradually incorporate samples into the adaptation process, managing large-scale datasets.
• Simultaneously considered sample-specific discrimination and set-based visual similarity within testing samples.

Main Results:

• The proposed model effectively adapts offline-learned identification models for online data.
• Demonstrated significant performance improvements on widely-used visual identification benchmarks.
• Showcased the model's suitability for various off-the-shelf visual identification baselines.

Conclusions:

• The online group-metric adaptation model offers a robust solution for visual identification challenges posed by distribution shifts.
• The integration of frequent sharing-subset mining and self-paced learning provides an effective strategy for online adaptation.
• This approach enhances the generalizability and performance of visual identification systems in real-world scenarios.