FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension

• URL: http://arxiv.org/abs/2409.14750v1
• Date: Mon, 23 Sep 2024 06:56:51 GMT
• Title: FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension
• Authors: Junzhuo Liu, Xuzheng Yang, Weiwei Li, Peng Wang,
• Abstract summary: Referring Expression (REC) is a crucial cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. We have established a new REC dataset characterized by two key features. It includes negative text and images created through fine-grained editing and generation based on existing data.
• Score: 10.482908189805872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Expression Comprehension (REC) is a crucial cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. Consequently, it serves as an ideal testing ground for Multi-modal Large Language Models (MLLMs). In pursuit of this goal, we have established a new REC dataset characterized by two key features: Firstly, it is designed with controllable varying levels of difficulty, necessitating multi-level fine-grained reasoning across object categories, attributes, and multi-hop relationships. Secondly, it includes negative text and images created through fine-grained editing and generation based on existing data, thereby testing the model's ability to correctly reject scenarios where the target object is not visible in the image--an essential aspect often overlooked in existing datasets and approaches. Utilizing this high-quality dataset, we conducted comprehensive evaluations of both state-of-the-art specialist models and MLLMs. Our findings indicate that there remains a significant gap in achieving satisfactory grounding performance. We anticipate that our dataset will inspire new approaches to enhance visual reasoning and develop more advanced cross-modal interaction strategies, ultimately unlocking the full potential of MLLMs. Our code and the datasets are available at https://github.com/liujunzhuo/FineCops-Ref.

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