Summary of Mllmreid: Multimodal Large Language Model-based Person Re-identification, by Shan Yang et al.
MLLMReID: Multimodal Large Language Model-based Person Re-identification
by Shan Yang, Yongfei Zhang
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 explores adapting multimodal large language models (MLLMs) for the task of ReID (Reidentification), which aims to identify and track individuals across images and videos. To address two apparent issues in fine-tuning MLLMs for ReID, the authors propose a method called MLLMReID. The approach leverages the ability of LLMs to continue writing by introducing Common Instruction, a simple way to design instructions for ReID without requiring diverse and complex instruction designs. Additionally, the paper proposes a multi-task learning-based synchronization module to ensure that the visual encoder of the MLLM is trained synchronously with the ReID task. The experimental results demonstrate the superiority of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of AI models called large language models (LLMs) for something called ReID, which means identifying people in pictures and videos. Right now, these LLMs are really good at doing lots of things, but they haven’t been tested much for ReID yet. The authors want to make it easier to use these models for ReID by giving them simple instructions instead of complicated ones. They also found a way to train the model’s “eyes” (what it looks at) and its “brain” (what it does with what it sees) at the same time, so they work better together. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Multi task
