Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective

TL;DR

This paper reformulates image retrieval re-ranking as a graph neural network, significantly reducing computational time and enabling real-time post-processing while maintaining or improving retrieval accuracy.

Contribution

It introduces a GNN-based re-ranking method that divides the process into graph construction and feature updating, achieving high efficiency and comparable or better results.

Findings

Accelerates re-ranking from 89.2s to 9.4ms on Market-1501

Achieves real-time processing with limited time cost

Maintains or improves retrieval accuracy on multiple benchmarks

Abstract

The re-ranking approach leverages high-confidence retrieved samples to refine retrieval results, which have been widely adopted as a post-processing tool for image retrieval tasks. However, we notice one main flaw of re-ranking, i.e., high computational complexity, which leads to an unaffordable time cost for real-world applications. In this paper, we revisit re-ranking and demonstrate that re-ranking can be reformulated as a high-parallelism Graph Neural Network (GNN) function. In particular, we divide the conventional re-ranking process into two phases, i.e., retrieving high-quality gallery samples and updating features. We argue that the first phase equals building the k-nearest neighbor graph, while the second phase can be viewed as spreading the message within the graph. In practice, GNN only needs to concern vertices with the connected edges. Since the graph is sparse, we can efficiently update the vertex features. On the Market-1501 dataset, we accelerate the re-ranking processing from 89.2s to 9.4ms with one K40m GPU, facilitating the real-time post-processing. Similarly, we observe that our method achieves comparable or even better retrieval results on the other four image retrieval benchmarks, i.e., VeRi-776, Oxford-5k, Paris-6k and University-1652, with limited time cost. Our code is publicly available.