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Bag of Tricks and A Strong baseline for Image Copy Detection

About

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.

Wenhao Wang, Weipu Zhang, Yifan Sun, Yi Yang• 2021

Related benchmarks

TaskDatasetResultRank
Image Copy DetectionGLIDE (test)
Average Similarity0.489
28
Image Copy DetectionSD 1.5 (test)
Average Similarity0.216
28
Image Copy DetectionMidjourney (test)
Average Similarity34.5
28
Image Copy DetectionDALL-E 2 (test)
Average Similarity0.346
28
Image Copy DetectionDeepFloyd IF (test)
Average Similarity47.7
28
Image Copy DetectionNew Bing (test)
Average Similarity0.338
28
Image Copy DetectionSDXL (test)
Avg Similarity40.1
28
ICDiffD-Rep (test)
PCC35.6
20
Image Copy Detection (Descriptor)DISC 2021 (test)
μAP71.5
14
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