Learning Visual Instance Retrieval from Failure: Efficient Online Local Metric Adaptation from Negative Samples.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Existing visual instance retrieval (VIR) methods often require extensive positive training pairs and struggle with data shifting for unseen samples.
• Current offline approaches for VIR face limitations due to the demanding nature of training data and performance constraints on novel data.

Purpose of the Study:

• To propose a novel paradigm for visual instance retrieval (VIR) that incorporates online metric adaptation with minimal computational cost.
• To develop an effective and efficient method for local metric adaptation using easily accessible negative samples.

Main Methods:

• The proposed method enables online metric adaptation by exploiting local hard negative samples to fine-tune the retrieval metric.
• This approach is designed to be generally applicable on top of existing offline-learned baseline models for VIR.
• Theoretical analyses are provided to ensure the asymptotic and practical reduction of classification error.

Main Results:

• The method demonstrates effectiveness in achieving optimal local metric adaptation.
• Experiments across various VIR tasks confirm the superiority of the proposed approach compared to existing methods.
• The approach successfully addresses the data shifting issue prevalent in unseen testing samples.

Conclusions:

• The proposed online local metric adaptation method offers a significant advancement in visual instance retrieval.
• This paradigm shift allows for more robust and adaptable VIR systems, particularly in scenarios with limited training data or significant data variations.