Summary of Ccup: a Controllable Synthetic Data Generation Pipeline For Pretraining Cloth-changing Person Re-identification Models, by Yujian Zhao et al.
CCUP: A Controllable Synthetic Data Generation Pipeline for Pretraining Cloth-Changing Person Re-Identification Models
by Yujian Zhao, Chengru Wu, Yinong Xu, Xuanzheng Du, Ruiyu Li, Guanglin Niu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of cloth-changing person re-identification (CC-ReID) in computer vision, a critical task that has garnered significant attention. Existing data-driven models are hindered by overfitting due to the high cost of constructing CC-ReID data. To address this issue, the authors propose a low-cost and efficient pipeline for generating synthetic data simulating real scenarios specific to the CC-ReID task. They introduce a new self-annotated CC-ReID dataset named Cloth-Changing Unreal Person (CCUP), containing 6,000 IDs, 1,179,976 images, 100 cameras, and 26.5 outfits per individual. The authors also present an effective pretrain-finetune framework for enhancing the generalization capabilities of traditional CC-ReID models. Experimental results show that two typical models, TransReID and FIRe^2, outperform other state-of-the-art models when integrated into this framework and trained on CCUP and benchmarks such as PRCC, VC-Clothes, and NKUP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better recognize people in different clothes. It’s a tricky problem because we don’t have enough data to train machines well. The authors came up with a clever way to create fake images that can help machines learn to recognize people even when they’re wearing different outfits. They made a big dataset called CCUP, which has lots of pictures of people in many different clothes. Then, they showed how this dataset can be used to improve the performance of machine learning models for person re-identification. |
Keywords
» Artificial intelligence » Attention » Generalization » Machine learning » Overfitting » Synthetic data
