Although current text detection algorithms demonstrate effectiveness in
general scenarios, their performance declines when confronted with
artistic-style text featuring complex structures. This paper proposes a method
that utilizes Criss-Cross Attention and residual dense block to address the
incomplete and misdiagnosis of artistic-style text detection by current
algorithms. Specifically, our method mainly consists of a feature extraction
backbone, a feature enhancement network, a multi-scale feature fusion module,
and a boundary discrimination module. The feature enhancement network
significantly enhances the model's perceptual capabilities in complex
environments by fusing horizontal and vertical contextual information, allowing
it to capture detailed features overlooked in artistic-style text. We
incorporate residual dense block into the Feature Pyramid Network to suppress
the effect of background noise during feature fusion. Aiming to omit the
complex post-processing, we explore a boundary discrimination module that
guides the correct generation of boundary proposals. Furthermore, given that
movie poster titles often use stylized art fonts, we collected a Movie-Poster
dataset to address the scarcity of artistic-style text data. Extensive
experiments demonstrate that our proposed method performs superiorly on the
Movie-Poster dataset and produces excellent results on multiple benchmark
datasets. The code and the Movie-Poster dataset will be available at:
this https URL

本文提出了一种利用交叉关注和残差密集块的方法，解决目前算法在具有复杂结构的艺术风格文本检测中存在的不完整和误诊问题。通过融合水平和垂直上下文信息来显著增强模型在复杂环境中的感知能力，并利用残差密集块来抑制背景噪声的影响，在不需要复杂的后处理的情况下引入边界鉴别模块来指导正确生成边界建议，实验证明所提出的方法在电影海报数据集上表现优异，并在多个基准数据集上获得出色的结果。

艺术风格文本检测器及一种新的电影海报数据集

Artistic-style text detector and a new Movie-Poster dataset

This paper proposes a novel framework for lung segmentation in chest X-rays.
It consists of two key contributions, a criss-cross attention based
segmentation network and radiorealistic chest X-ray image synthesis (i.e. a
synthesized radiograph that appears anatomically realistic) for data
augmentation. The criss-cross attention modules capture rich global contextual
information in both horizontal and vertical directions for all the pixels thus
facilitating accurate lung segmentation. To reduce the manual annotation burden
and to train a robust lung segmentor that can be adapted to pathological lungs
with hazy lung boundaries, an image-to-image translation module is employed to
synthesize radiorealistic abnormal CXRs from the source of normal ones for data
augmentation. The lung masks of synthetic abnormal CXRs are propagated from the
segmentation results of their normal counterparts, and then serve as pseudo
masks for robust segmentor training. In addition, we annotate 100 CXRs with
lung masks on a more challenging NIH Chest X-ray dataset containing both
posterioranterior and anteroposterior views for evaluation. Extensive
experiments validate the robustness and effectiveness of the proposed
framework. The code and data can be found from
this https URL .

本研究提出了一种新的肺部分割框架，包括基于交叉关注的分割网络和放射真实胸部 X 射线图像合成用于数据增强。同时，使用图像到图像的翻译模块来合成放射学异常胸片器用于数据增强，并阐明了该框架的稳健性和有效性。