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arXiv · 2111.07090

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

Wenhao Wang·Yifan Sun·Weipu Zhang·Yi Yang
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Abstract

Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D$^2$LV, 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 https://github.com/WangWenhao0716/ISC-Track1-Submission.

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Wenhao Wang, Yifan Sun, Weipu Zhang, Yi Yang. 2021-11-13. D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection. https://arxiv.org/abs/2111.07090

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