Vision-Language Models Struggle to Align Entities across Modalities
This addresses a gap in cross-modal entity linking, which is crucial for applications like multimodal code generation and fake news detection, but the work is incremental as it focuses on benchmarking and analysis.
The paper introduces MATE, a new benchmark with 5.5k instances for cross-modal entity linking, and finds that state-of-the-art Vision-Language Models struggle significantly compared to humans, especially with more objects in scenes.
Cross-modal entity linking refers to the ability to align entities and their attributes across different modalities. While cross-modal entity linking is a fundamental skill needed for real-world applications such as multimodal code generation, fake news detection, or scene understanding, it has not been thoroughly studied in the literature. In this paper, we introduce a new task and benchmark to address this gap. Our benchmark, MATE, consists of 5.5k evaluation instances featuring visual scenes aligned with their textual representations. To evaluate cross-modal entity linking performance, we design a question-answering task that involves retrieving one attribute of an object in one modality based on a unique attribute of that object in another modality. We evaluate state-of-the-art Vision-Language Models (VLMs) and humans on this task, and find that VLMs struggle significantly compared to humans, particularly as the number of objects in the scene increases. Our analysis also shows that, while chain-of-thought prompting can improve VLM performance, models remain far from achieving human-level proficiency. These findings highlight the need for further research in cross-modal entity linking and show that MATE is a strong benchmark to support that progress.