Summary of Lifelong Person Search, by Jae-won Yang et al.
Lifelong Person Search
by Jae-Won Yang, Seungbin Hong, Jae-Young Sim
First submitted to arxiv on: 31 Jul 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 a crucial issue in person search, where existing methods are designed to work on a single target dataset. However, in real-world applications, diverse datasets are constantly being added, leading to catastrophic forgetting of previously learned knowledge. The authors propose a novel lifelong person search (LPS) framework that incrementally trains models on new datasets while preserving knowledge from old ones. This is achieved through end-to-end knowledge distillation and rehearsal-based instance matching. Experimental results show significant improvements in detection and re-identification performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines remember what they learned before, even when faced with new information. Imagine having a personal assistant that can recognize people in old photos and keep improving its skills over time! The researchers developed a way for computers to learn from many datasets, not just one, by using old knowledge to help them understand new data. |
Keywords
* Artificial intelligence * Knowledge distillation
