Significant progress has been made in scene text detection models since the
rise of deep learning, but scene text layout analysis, which aims to group
detected text instances as paragraphs, has not kept pace. Previous works either
treated text detection and grouping using separate models, or train a model
from scratch while using a unified one. All of them have not yet made full use
of the already well-trained text detectors and easily obtainable detection
datasets. In this paper, we present Text Grouping Adapter (TGA), a module that
can enable the utilization of various pre-trained text detectors to learn
layout analysis, allowing us to adopt a well-trained text detector right off
the shelf or just fine-tune it efficiently. Designed to be compatible with
various text detector architectures, TGA takes detected text regions and image
features as universal inputs to assemble text instance features. To capture
broader contextual information for layout analysis, we propose to predict text
group masks from text instance features by one-to-many assignment. Our
comprehensive experiments demonstrate that, even with frozen pre-trained
models, incorporating our TGA into various pre-trained text detectors and text
spotters can achieve superior layout analysis performance, simultaneously
inheriting generalized text detection ability from pre-training. In the case of
full parameter fine-tuning, we can further improve layout analysis performance.

利用 Text Grouping Adapter（TGA）模块，将各种预训练文本检测器有效地用于学习布局分析，达到优秀的布局分析性能，同时继承预训练的普适文本检测能力。

文本分组适配器：为布局分析适配预训练的文本检测器

Text Grouping Adapter: Adapting Pre-trained Text Detector for Layout  Analysis

Most dialogue systems in real world rely on predefined intents and answers
for QA service, so discovering potential intents from large corpus previously
is really important for building such dialogue services. Considering that most
scenarios have few intents known already and most intents waiting to be
discovered, we focus on semi-supervised text clustering and try to make the
proposed method benefit from labeled samples for better overall clustering
performance. In this paper, we propose Deep Contrastive Semi-supervised
Clustering (DCSC), which aims to cluster text samples in a semi-supervised way
and provide grouped intents to operation staff. To make DCSC fully utilize the
limited known intents, we propose a two-stage training procedure for DCSC, in
which DCSC will be trained on both labeled samples and unlabeled samples, and
achieve better text representation and clustering performance. We conduct
experiments on two public datasets to compare our model with several popular
methods, and the results show DCSC achieve best performance across all datasets
and circumstances, indicating the effect of the improvements in our work.

DCSC 是一种半监督的文本聚类方法，可对话系统中的文本样本进行聚类，通过两阶段训练过程利用标记样本，取得最佳聚类性能，从而在文本归类方面取得了最佳性能。