MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs

• URL: http://arxiv.org/abs/2410.12332v1
• Date: Wed, 16 Oct 2024 07:52:57 GMT
• Title: MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs
• Authors: Yunqiu Xu, Linchao Zhu, Yi Yang,
• Abstract summary: This paper proposes a new visual grounding task called multi-context visual grounding. It aims to localize instances of interest across multiple images based on open-ended text prompts. We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities.
• Score: 61.56904387052982
• License:
• Abstract: While multimodal large language models (MLLMs) have demonstrated extraordinary vision-language understanding capabilities and shown potential to serve as general-purpose assistants, their abilities to solve instance-level visual-language problems beyond a single image warrant further exploration. In order to assess these unproven abilities of MLLMs, this paper proposes a new visual grounding task called multi-context visual grounding, which aims to localize instances of interest across multiple images based on open-ended text prompts. To facilitate this research, we meticulously construct a new dataset MC-Bench for benchmarking the visual grounding capabilities of MLLMs. MC-Bench features 2K high-quality and manually annotated samples, consisting of instance-level labeled image pairs and corresponding text prompts that indicate the target instances in the images. In total, there are three distinct styles of text prompts, covering 20 practical skills. We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities. Our evaluation reveals a non-trivial performance gap between existing MLLMs and humans across all metrics. We also observe that existing MLLMs typically outperform foundation models without LLMs only on image-level metrics, and the specialist MLLMs trained on single images often struggle to generalize to multi-image scenarios. Moreover, a simple stepwise baseline integrating advanced MLLM and a detector can significantly surpass prior end-to-end MLLMs. We hope our MC-Bench and empirical findings can encourage the research community to further explore and enhance the untapped potentials of MLLMs in instance-level tasks, particularly in multi-image contexts. Project page: https://xuyunqiu.github.io/MC-Bench/.

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