Summary of Synthesizing Efficient Data with Diffusion Models For Person Re-identification Pre-training, by Ke Niu et al.
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training
by Ke Niu, Haiyang Yu, Xuelin Qian, Teng Fu, Bin Li, Xiangyang Xue
First submitted to arxiv on: 10 Jun 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 The paper proposes a novel paradigm called Diffusion-ReID to efficiently augment and generate diverse images for person re-identification (Re-ID) tasks. The approach unfolds in two stages: generation and filtering. During the generation stage, Language Prompts Enhancement (LPE) is used to ensure ID consistency between input image sequences and generated images. A Diversity Injection (DI) module is then applied to increase attribute diversity. To remove low-quality images, a Re-ID confidence threshold filter is used. The paper creates a new large-scale person Re-ID dataset called Diff-Person, which consists of over 777K images from 5,183 identities. This dataset is used to pre-train a stronger person Re-ID backbone. Extensive experiments are conducted on four person Re-ID benchmarks in six widely used settings, showing significant superiority compared to other pre-training and self-supervised competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to make computers better at recognizing people in photos. Right now, these computers have trouble because they’re only trained on a small set of pictures from the internet. This makes them bad at recognizing people with different clothes or haircuts. The new approach uses words and computer tricks to create many more pictures that are similar to the ones we already have. These new pictures help train the computer to be better at recognizing people. The paper even creates its own big set of pictures for training, which is much bigger than any other set used before. When tested on different sets of pictures, this approach does a lot better than others. |
Keywords
» Artificial intelligence » Diffusion » Self supervised
