About this scholarly article

Threshold-Consistent Margin Loss for Open-World Deep Metric Learning by Zhang, Qin; Xu, Linghan; Tang, Qingming; Fang, Jun; Wu, Ying Nian; Tighe, Joe; Xing, Yifan is a scholarly article available to read on EtoBox.

Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes and data distributions. When combined with the common practice of using a fixed threshold to declare a match, this gives rise to significant performance variations in terms of false accept rate (FAR) and false reject rate (FRR) across test classes and data distributions. We define this issue in DML as threshold inconsistency. In real-world applications, such inconsistency often complicates the threshold selection process when deploying commercial image retrieval systems. To measure this inconsistency, we propose a novel variance-based metric called Operating-Point-Inconsistency-Score (OPIS) that quantifies the variance in the operating characteristics across classes. Using the OPIS metric, we find that achieving high accuracy levels in a DML model does not automatically guarantee threshold consistency. In fact, our investigation reveals a Pareto frontier in the high-accuracy regime, where existing methods to improve accuracy often lead to degradation in threshold consistency. To address this trade-off, we i

Author

Zhang, Qin; Xu, Linghan; Tang, Qingming; Fang, Jun; Wu, Ying Nian; Tighe, Joe; Xing, Yifan

Published

2023

Language

EN