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

Change detection, as a research hotspot in the field of remote sensing, has
witnessed continuous development and progress. However, the discrimination of
boundary details remains a significant bottleneck due to the complexity of
surrounding elements between change areas and backgrounds. Discriminating the
boundaries of large change areas results in misalignment, while connecting
boundaries occurs for small change targets. To address the above issues, a
novel network based on the localization-then-refinement strategy is proposed in
this paper, namely LRNet. LRNet consists of two stages: localization and
refinement. In the localization stage, a three-branch encoder simultaneously
extracts original image features and their differential features for
interactive localization of the position of each change area. To minimize
information loss during feature extraction, learnable optimal pooling (LOP) is
proposed to replace the widely used max-pooling. Additionally, this process is
trainable and contributes to the overall optimization of the network. To
effectively interact features from different branches and accurately locate
change areas of various sizes, change alignment attention (C2A) and
hierarchical change alignment module (HCA) are proposed. In the refinement
stage, the localization results from the localization stage are corrected by
constraining the change areas and change edges through the edge-area alignment
module (E2A). Subsequently, the decoder, combined with the difference features
strengthened by C2A in the localization phase, refines change areas of
different sizes, ultimately achieving accurate boundary discrimination of
change areas. The proposed LRNet outperforms 13 other state-of-the-art methods
in terms of comprehensive evaluation metrics and provides the most precise
boundary discrimination results on the LEVIR-CD and WHU-CD datasets.

本文提出了基于定位再细化策略的新型网络 LRNet，通过定位和细化阶段，使用学习优化池化（LOP）和变换对齐注意力（C2A）等方法，提高了改变区域的定位和边界判别准确性，并在 LEVIR-CD 和 WHU-CD 数据集上取得了最精确的边界判别结果。