A Benchmark on Tricks for Large-scale Image Retrieval

TL;DR

This paper systematically evaluates the impact of pre-processing and post-processing tricks on large-scale image retrieval performance, demonstrating significant improvements without complex models.

Contribution

It provides a comprehensive analysis of tricks for large-scale image retrieval, highlighting their effectiveness and practical benefits.

Findings

Proper tricks can greatly enhance retrieval accuracy

Combining tricks yields better performance than individual ones

Achieved competitive results on Google Landmark Retrieval Challenge 2019

Abstract

Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval. Meanwhile, less attention has been paid to pre-processing and post-processing tricks that can significantly boost performance. Furthermore, we found that most previous studies used small scale datasets to simplify processing. Because the behavior of a feature representation in a deep learning model depends on both domain and data, it is important to understand how model behave in large-scale environments when a proper combination of retrieval tricks is used. In this paper, we extensively analyze the effect of well-known pre-processing, post-processing tricks, and their combination for large-scale image retrieval. We found that proper use of these tricks can significantly improve model performance without necessitating complex architecture or introducing loss, as confirmed by achieving a competitive result on the Google Landmark Retrieval Challenge 2019.