  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.
