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Multimodal LLMs for Analyzing Large Image Collections: A New Approach to Trend Detection

Analyzing large image datasets, especially those documenting temporal changes, presents a challenge. Traditional methods often reach their limits when it comes to extracting patterns and trends from millions of images without predefined search criteria or training data. A promising approach to overcoming this challenge lies in the use of multimodal Large Language Models (MLLMs).

The Challenge of Scalability

MLLMs have the ability to link and interpret semantic information from different modalities, such as text and images. This allows them to understand complex relationships and answer open-ended questions without relying on specific training data. However, the sheer size of the datasets to be analyzed poses a significant problem. MLLMs can typically only process a limited amount of contextual information, far below the scale of millions of images.

A Bottom-Up Approach to Problem Solving

To address this scaling challenge, a bottom-up approach has been developed that breaks down the complex analysis into smaller, manageable sub-problems. Specific MLLM-based solutions are used for each of these sub-problems. This approach allows the strengths of MLLMs to be used effectively without being overwhelmed by the amount of data.

How the System Works

The system analyzes images taken over a certain period to identify recurring changes and trends. Instead of searching for predefined patterns, the system can answer open-ended questions, such as: "What kinds of changes frequently occur in the city?". This approach differs fundamentally from previous visual analysis methods, which are usually based on predefined categories or supervised learning.

Experimental Results and Outlook

Experiments and ablation studies have shown that the system delivers significantly better results compared to conventional methods. It successfully identified interesting trends in image data of large cities, such as the increase in outdoor seating in restaurants or the repainting of bridges. These results highlight the potential of MLLMs for analyzing large image collections and detecting temporal changes.

Potentials and Future Developments

The application of MLLMs in visual analysis opens up new possibilities for researching and understanding complex datasets. The ability to answer open-ended questions and identify trends without prior assumptions offers enormous potential for various application areas, from urban planning and environmental monitoring to market research. Future research could focus on improving the scalability and efficiency of the system, as well as expanding the application possibilities.

Bibliography: - https://arxiv.org/abs/2504.08727 - https://deeplearn.org/arxiv/595149/visual-chronicles:-using-multimodal-llms-to-analyze-massive-collections-of-images - https://synthical.com/article/Visual-Chronicles%3A-Using-Multimodal-LLMs-to-Analyze-Massive-Collections-of-Images-9f556e3e-21ae-4642-a248-e7cc107bf4a0? - https://chatpaper.com/chatpaper/zh-CN/paper/128518 - https://chatpaper.com/chatpaper/?id=4&date=1744560000&page=1 - https://paperreading.club/page?id=298913 - https://arxiv.org/html/2412.03665v1 - https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models - https://en.wikipedia.org/wiki/Large_language_model ```