Human Re-ID Meets LVLMs: What can we expect?
This work addresses the problem of evaluating LVLMs in a specific domain (human re-identification) for researchers and practitioners, but it is incremental as it primarily benchmarks existing models without introducing new methods.
The paper compared the performance of leading large vision-language models (LVLMs) like ChatGPT-4o and Gemini-2.0-Flash to a state-of-the-art ReID PersonViT model on the Market1501 dataset for human re-identification, finding that LVLMs have strengths but severe limitations, including catastrophic answers.
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.