2021/11/13 by Wenhao Wang, Yifan Sun, Wang, Wenhao +5
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2111.07090
openalex publication_date 2021/11/13 · openalex created_date 2021/11/22 · openalex updated_date 2026/07/28
Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D2LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D2LV, 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.