MMRel: A Relation Understanding Dataset and Benchmark in the MLLM Era

• URL: http://arxiv.org/abs/2406.09121v1
• Date: Thu, 13 Jun 2024 13:51:59 GMT
• Title: MMRel: A Relation Understanding Dataset and Benchmark in the MLLM Era
• Authors: Jiahao Nie, Gongjie Zhang, Wenbin An, Yap-Peng Tan, Alex C. Kot, Shijian Lu,
• Abstract summary: Multi-Modal Relation Understanding (MMRel) is a comprehensive dataset for studying inter-object relations with Multi-modal Large Language Models (MLLMs) MMRel features three distinctive attributes: (i) It includes over 15K question-answer pairs, which are sourced from three distinct domains, ensuring large scale and high diversity; (ii) It contains a subset featuring highly unusual relations, on which MLLMs often fail due to hallucinations, thus are very challenging; (iii) It provides manually verified high-quality labels for inter-object relations.
• Score: 72.95901753186227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the recent advancements in Multi-modal Large Language Models (MLLMs), understanding inter-object relations, i.e., interactions or associations between distinct objects, remains a major challenge for such models. This issue significantly hinders their advanced reasoning capabilities and is primarily due to the lack of large-scale, high-quality, and diverse multi-modal data essential for training and evaluating MLLMs. In this paper, we provide a taxonomy of inter-object relations and introduce Multi-Modal Relation Understanding (MMRel), a comprehensive dataset designed to bridge this gap by providing large-scale, high-quality and diverse data for studying inter-object relations with MLLMs. MMRel features three distinctive attributes: (i) It includes over 15K question-answer pairs, which are sourced from three distinct domains, ensuring large scale and high diversity; (ii) It contains a subset featuring highly unusual relations, on which MLLMs often fail due to hallucinations, thus are very challenging; (iii) It provides manually verified high-quality labels for inter-object relations. Thanks to these features, MMRel is ideal for evaluating MLLMs on relation understanding, as well as being used to fine-tune MLLMs to enhance relation understanding and even benefit overall performance in various vision-language tasks. Extensive experiments on various popular MLLMs validate the effectiveness of MMRel. Both MMRel dataset and the complete labeling scripts have been made publicly available.

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