Semantic Correspondence: Unified Benchmarking and a Strong Baseline

TL;DR

This paper provides the first comprehensive survey and analysis of semantic correspondence methods in computer vision, introduces a unified benchmark, and proposes a strong baseline that achieves state-of-the-art results.

Contribution

It offers a taxonomy of existing methods, a unified benchmark with comparative results, and a simple baseline that sets new performance standards.

Findings

Unified benchmarking enables fair comparison of methods.

The proposed baseline achieves state-of-the-art performance.

Component analysis reveals key factors influencing accuracy.

Abstract

Establishing semantic correspondence is a challenging task in computer vision, aiming to match keypoints with the same semantic information across different images. Benefiting from the rapid development of deep learning, remarkable progress has been made over the past decade. However, a comprehensive review and analysis of this task remains absent. In this paper, we present the first extensive survey of semantic correspondence methods. We first propose a taxonomy to classify existing methods based on the type of their method designs. These methods are then categorized accordingly, and we provide a detailed analysis of each approach. Furthermore, we aggregate and summarize the results of methods in literature across various benchmarks into a unified comparative table, with detailed configurations to highlight performance variations. Additionally, to provide a detailed understanding on existing methods for semantic matching, we thoroughly conduct controlled experiments to analyse the effectiveness of the components of different methods. Finally, we propose a simple yet effective baseline that achieves state-of-the-art performance on multiple benchmarks, providing a solid foundation for future research in this field. We hope this survey serves as a comprehensive reference and consolidated baseline for future development. Code is publicly available at: https://github.com/Visual-AI/Semantic-Correspondence.