Image copy detection is of great importance in real-life social media. In this paper, a bag of tricks and a strong baseline are proposed for image copy detection. Unsupervised pre-training substitutes the commonly-used supervised one. Beyond that, we design a descriptor stretching strategy to stabilize the scores of different queries. Experiments demonstrate that the proposed method is effective. The proposed baseline ranks third out of 526 participants on the Facebook AI Image Similarity Challenge: Descriptor Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track2-Submission.
Bag of Tricks and A Strong baseline for Image Copy Detection
A method for image copy detection using unsupervised pre-training and a descriptor stretching strategy achieves effective results, ranking third in a Facebook AI challenge.
- Year
- 2021
- Venue
- arXiv 2021
- Authors
- 4
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2111.08004v2ARXIV-DEFAULT
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