Summary of From Attributes to Natural Language: a Survey and Foresight on Text-based Person Re-identification, by Fanzhi Jiang et al.
From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification
by Fanzhi Jiang, Su Yang, Mark W. Jones, Liumei Zhang
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 proposes a comprehensive survey of text-based person re-identification (Re-ID) from a technical perspective. The authors introduce a taxonomy spanning Evaluation, Strategy, Architecture, and Optimization dimensions to summarize the task. They begin by laying the groundwork for text-based person Re-ID, explaining fundamental concepts related to attribute/natural language-based identification. The paper also presents an examination of existing benchmark datasets and metrics, feature extraction strategies, network architectures, and loss functions used in text-based person Re-ID research. The authors conclude by summarizing their findings, highlighting challenges in the field, and outlining potential avenues for future open-set text-based person Re-ID. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about recognizing specific people from text descriptions. It’s like trying to find a friend in a crowd using only what they look like or what they’re wearing. The authors want to make sure that all the important research on this topic is collected and organized, so they created a special way to group it together. They start by explaining the basics of how we identify people from text descriptions, then they talk about the different ways scientists have tried to do this. Finally, they highlight some problems with current methods and suggest new ideas for making it better. |
Keywords
» Artificial intelligence » Feature extraction » Optimization
