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D^2LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

2021-11-13 10:56:58
Wenhao Wang, Yifan Sun, Weipu Zhang, Yi Yang
arXiv_CV
             
Abstract

Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D^2LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D^2LV, unsupervised pre-training substitutes the commonly-used supervised one. When training, we design a set of basic and six advanced transformations, and a simple but effective baseline learns robust representation. During testing, a global-local and local-global matching strategy is proposed. The strategy performs local-verification between reference and query images. Experiments demonstrate that the proposed method is effective. The proposed approach ranks first out of 1,103 participants on the Facebook AI Image Similarity Challenge: Matching Track. The code and trained models are available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2111.07090

PDF

https://arxiv.org/pdf/2111.07090.pdf

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