Summary of Domain Consistency Representation Learning For Lifelong Person Re-identification, by Shiben Liu et al.
Domain Consistency Representation Learning for Lifelong Person Re-Identification
by Shiben Liu, Qiang Wang, Huijie Fan, Weihong Ren, Baojie Fan, Yandong Tang
First submitted to arxiv on: 30 Sep 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 addresses the lifelong person re-identification (LReID) problem, which is characterized by a contradictory relationship between intra-domain discrimination and inter-domain gaps when learning from continuous data. The authors propose a novel domain consistency representation learning (DCR) model that balances these two aspects to improve LReID performance. Specifically, they explore global and attribute-wise representations as a bridge to address this challenge. At the intra-domain level, they develop an attribute-oriented anti-forgetting (AF) strategy to enhance inter-domain consistency. Additionally, they propose a knowledge consolidation (KC) strategy to facilitate knowledge transfer. Experimental results show that their DCR model outperforms state-of-the-art LReID methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky problem in computer vision called lifelong person re-identification. It’s about recognizing people from photos taken at different times, even if they wear different clothes or have changed hairstyles. The researchers propose a new way to make this recognition better by combining two important ideas: making sure the model is good at recognizing small differences between similar faces (intra-domain discrimination), and making sure it can recognize people across different cameras or environments (inter-domain gaps). They also develop special techniques to prevent their model from forgetting what it learned earlier. The results show that this new approach works better than other methods for this task. |
Keywords
» Artificial intelligence » Representation learning
