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。

通过视觉 - 非对称一致性学习在文档图像中增强的语义实体识别

Vision-Enhanced Semantic Entity Recognition in Document Images via  Visually-Asymmetric Consistency Learning

Single-image haze-removal is challenging due to limited information contained
in one single image. Previous solutions largely rely on handcrafted priors to
compensate for this deficiency. Recent convolutional neural network (CNN)
models have been used to learn haze-related priors but they ultimately work as
advanced image filters. In this paper we propose a novel semantic ap- proach
towards single image haze removal. Unlike existing methods, we infer color
priors based on extracted semantic features. We argue that semantic context can
be exploited to give informative cues for (a) learning color prior on clean
image and (b) estimating ambient illumination. This design allowed our model to
recover clean images from challenging cases with strong ambiguity, e.g.
saturated illumination color and sky regions in image. In experiments, we
validate our ap- proach upon synthetic and real hazy images, where our method
showed superior performance over state-of-the-art approaches, suggesting
semantic information facilitates the haze removal task.

本文提出一种基于语义特征推断颜色先验和估计周围照明的新方法，比单纯使用卷积神经网络学习雾霾相关先验的方法更能有效地去除单张图像中的雾霾。实验证明，该语义方法在合成图像和真实雾霾图像上显示了较高的性能。