arXiv:2406.03701v1 Announce Type: new
Abstract: In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply recognizing and analyzing cross-modal information. To address this, this work for the first time introduces the concept of grounded Multimodal Universal Information Extraction (MUIE), providing a unified task framework to analyze any IE tasks over various modalities, along with their fine-grained groundings. To tackle MUIE, we tailor a multimodal large language model (MLLM), Reamo, capable of extracting and grounding information from all modalities, i.e., recognizing everything from all modalities at once. Reamo is updated via varied tuning strategies, equipping it with powerful capabilities for information recognition and fine-grained multimodal grounding. To address the absence of a suitable benchmark for grounded MUIE, we curate a high-quality, diverse, and challenging test set, which encompasses IE tasks across 9 common modality combinations with the corresponding multimodal groundings. The extensive comparison of Reamo with existing MLLMs integrated into pipeline approaches demonstrates its advantages across all evaluation dimensions, establishing a strong benchmark for the follow-up research. Our resources are publicly released at https://haofei.vip/MUIE.
Introducing Grounded Multimodal Universal Information Extraction (MUIE)
In recent years, there has been a growing focus on information extraction (IE), but most studies have examined individual modalities in isolation. This approach has limited our understanding and analysis of cross-modal information. However, a new concept called Grounded Multimodal Universal Information Extraction (MUIE) aims to bridge this gap by providing a unified framework for analyzing IE tasks across various modalities and their fine-grained groundings.
The concept of MUIE is innovative because it recognizes the importance of considering multiple modalities in information extraction. Modalities can include text, images, audio, video, and other forms of data. By analyzing and extracting information from multiple modalities simultaneously, MUIE offers a more comprehensive understanding of complex cross-modal information.
Reamo: A Multimodal Large Language Model (MLLM)
To address the challenges of MUIE, the research team behind this work has developed a multimodal large language model called Reamo. Reamo is designed to extract and ground information from all modalities, effectively recognizing and understanding the content from different sources at once.
What sets Reamo apart is its ability to be updated and tuned using varied strategies. This ensures that it remains equipped with powerful capabilities for information recognition and fine-grained multimodal grounding, even as new data and modalities emerge.
A Benchmark for Grounded MUIE
One of the key contributions of this work is the creation of a benchmark for grounded MUIE. The research team has curated a high-quality, diverse, and challenging test set that encompasses IE tasks across nine common modality combinations. Each task in the test set comes with the corresponding multimodal groundings, providing a thorough evaluation of the performance of Reamo and other MLLMs integrated into pipeline approaches.
Implications and Future Research
The introduction of grounded MUIE and the development of Reamo open up exciting possibilities for the field of multimedia information systems. By considering and analyzing multiple modalities simultaneously, researchers and practitioners can gain a deeper understanding of complex information and improve the accuracy and effectiveness of information extraction tools and techniques.
This work has implications for various areas related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. For example, in the field of virtual reality, the ability to extract and ground information from multiple modalities can enhance the immersive experience and create more realistic virtual environments.
As future research builds upon this work, we can expect to see advancements in the development of multimodal large language models and the refinement of grounded MUIE techniques. This will lead to improved information extraction across diverse modalities and pave the way for new applications and innovations in the broader field of multimedia information systems.
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This article has been written based on the paper “Grounded Multimodal Universal Information Extraction” by the authors.
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