# Human Re-ID Meets LVLMs: What can we expect?

arXiv: https://arxiv.org/abs/2501.18698 · HTML: https://mlsanity.com/paper/2501.18698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Contextual Object Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2305.18279/) (2023)
- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics](https://mlsanity.com/paper/2403.14077/) (2024)
- [HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?](https://mlsanity.com/paper/2403.02727/) (2024)
- [Prompting Visual-Language Models for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2308.13382/) (2023)
- [ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities](https://mlsanity.com/paper/2403.02965/) (2024)
- [ChatGPT Meets Iris Biometrics](https://mlsanity.com/paper/2408.04868/) (2024)
- [PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-Identification](https://mlsanity.com/paper/2408.05398/) (2024)
- [PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders](https://mlsanity.com/paper/2311.04496/) (2023)
- [Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age](https://mlsanity.com/paper/2410.24148/) (2024)
- [GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM](https://mlsanity.com/paper/2407.10870/) (2024)

## Built on by (most cited first, 1 of 1)

- [Evolution of ReID: From Early Methods to LLM Integration](https://mlsanity.com/paper/2506.13039/) (2025)

## Abstract (arXiv)

Large vision-language models (LVLMs) have been regarded as a breakthrough advance in an astoundingly variety of tasks, from content generation to virtual assistants and multimodal search or retrieval. However, for many of these applications, the performance of these methods has been widely criticized, particularly when compared with state-of-the-art methods and technologies in each specific domain. In this work, we compare the performance of the leading large vision-language models in the human re-identification task, using as baseline the performance attained by state-of-the-art AI models specifically designed for this problem. We compare the results due to ChatGPT-4o, Gemini-2.0-Flash, Claude 3.5 Sonnet, and Qwen-VL-Max to a baseline ReID PersonViT model, using the well-known Market1501 dataset. Our evaluation pipeline includes the dataset curation, prompt engineering, and metric selection to assess the models' performance. Results are analyzed from many different perspectives: similarity scores, classification accuracy, and classification metrics, including precision, recall, F1 score, and area under curve (AUC). Our results confirm the strengths of LVLMs, but also their severe limitations that often lead to catastrophic answers and should be the scope of further research. As a concluding remark, we speculate about some further research that should fuse traditional and LVLMs to combine the strengths from both families of techniques and achieve solid improvements in performance.