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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Image Copy Detection | GLIDE (test) | Average Similarity0.489 | 28 | |
| Image Copy Detection | SD 1.5 (test) | Average Similarity0.216 | 28 | |
| Image Copy Detection | Midjourney (test) | Average Similarity34.5 | 28 | |
| Image Copy Detection | DALL-E 2 (test) | Average Similarity0.346 | 28 | |
| Image Copy Detection | DeepFloyd IF (test) | Average Similarity47.7 | 28 | |
| Image Copy Detection | New Bing (test) | Average Similarity0.338 | 28 | |
| Image Copy Detection | SDXL (test) | Avg Similarity40.1 | 28 | |
| ICDiff | D-Rep (test) | PCC35.6 | 20 | |
| Image Copy Detection (Descriptor) | DISC 2021 (test) | μAP71.5 | 14 |
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