Extracting meaningful entities belonging to predefined categories from
Visually-rich Form-like Documents (VFDs) is a challenging task. Visual and
layout features such as font, background, color, and bounding box location and
size provide important cues for identifying entities of the same type. However,
existing models commonly train a visual encoder with weak cross-modal
supervision signals, resulting in a limited capacity to capture these
non-textual features and suboptimal performance. In this paper, we propose a
novel \textbf{V}isually-\textbf{A}symmetric co\textbf{N}sisten\textbf{C}y
\textbf{L}earning (\textsc{Vancl}) approach that addresses the above limitation
by enhancing the model's ability to capture fine-grained visual and layout
features through the incorporation of color priors. Experimental results on
benchmark datasets show that our approach substantially outperforms the strong
LayoutLM series baseline, demonstrating the effectiveness of our approach.
Additionally, we investigate the effects of different color schemes on our
approach, providing insights for optimizing model performance. We believe our
work will inspire future research on multimodal information extraction.

从视觉丰富的表单样式文档（VFDs）中提取属于预定义类别的有意义实体是一项具有挑战性的任务。本文提出了一种全新的 Vancl 方法，通过引入颜色先验知识来增强模型对细粒度视觉和布局特征的捕捉能力，并在基准数据集上实验证明我们的方法明显优于强大的 LayoutLM 系列基线，展示了该方法的有效性，并通过研究不同的颜色方案对该方法的影响，为优化模型性能提供了启示。我们相信我们的工作将激发未来的多模态信息提取研究。