The adaptation capability to a wide range of domains is crucial for scene
text spotting models when deployed to real-world conditions. However, existing
state-of-the-art (SOTA) approaches usually incorporate scene text detection and
recognition simply by pretraining on natural scene text datasets, which do not
directly exploit the intermediate feature representations between multiple
domains. Here, we investigate the problem of domain-adaptive scene text
spotting, i.e., training a model on multi-domain source data such that it can
directly adapt to target domains rather than being specialized for a specific
domain or scenario. Further, we investigate a transformer baseline called
Swin-TESTR to focus on solving scene-text spotting for both regular and
arbitrary-shaped scene text along with an exhaustive evaluation. The results
clearly demonstrate the potential of intermediate representations to achieve
significant performance on text spotting benchmarks across multiple domains
(e.g. language, synth-to-real, and documents). both in terms of accuracy and
efficiency.

基于中间表示的领域自适应场景文本识别模型，以达到在各个领域中的高准确性和高效率。

利用多语言数据集进行预训练的能力：提升文本定位性能

Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards  Enhancing Text Spotting Performance

Incorporating auxiliary modalities such as images into event detection models
has attracted increasing interest over the last few years. The complexity of
natural language in describing situations has motivated researchers to leverage
the related visual context to improve event detection performance. However,
current approaches in this area suffer from data scarcity, where a large amount
of labelled text-image pairs are required for model training. Furthermore,
limited access to the visual context at inference time negatively impacts the
performance of such models, which makes them practically ineffective in
real-world scenarios. In this paper, we present a novel domain-adaptive
visually-fused event detection approach that can be trained on a few labelled
image-text paired data points. Specifically, we introduce a visual imaginator
method that synthesises images from text in the absence of visual context.
Moreover, the imaginator can be customised to a specific domain. In doing so,
our model can leverage the capabilities of pre-trained vision-language models
and can be trained in a few-shot setting. This also allows for effective
inference where only single-modality data (i.e. text) is available. The
experimental evaluation on the benchmark M2E2 dataset shows that our model
outperforms existing state-of-the-art models, by up to 11 points.

本研究提出一种领域特定的、可从少量标注的图文数据训练的、可在缺乏可视化背景下合成图像的视觉联想事件检测方法，实验结果表明，在 M2E2 基准数据集上，该模型的性能超过了现有的最先进模型达 11 个百分点。

文本驱动的少样本领域自适应视觉融合事件检测

Few-shot Domain-Adaptive Visually-fused Event Detection from Text

Recent works on fake news detection have shown the efficacy of using emotions
as a feature for improved performance. However, the cross-domain impact of
emotion-guided features for fake news detection still remains an open problem.
In this work, we propose an emotion-guided, domain-adaptive, multi-task
approach for cross-domain fake news detection, proving the efficacy of
emotion-guided models in cross-domain settings for various datasets.

本研究提出了一种情绪引导的、领域自适应的、多任务方法，用于各种数据集中的跨领域假新闻检测，证明了情感引导模型在跨领域设置中的有效性。

情感引导下的领域自适应假新闻检测方法：对抗学习

An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning

Language models pretrained on text from a wide variety of sources form the
foundation of today's NLP. In light of the success of these broad-coverage
models, we investigate whether it is still helpful to tailor a pretrained model
to the domain of a target task. We present a study across four domains
(biomedical and computer science publications, news, and reviews) and eight
classification tasks, showing that a second phase of pretraining in-domain
(domain-adaptive pretraining) leads to performance gains, under both high- and
low-resource settings. Moreover, adapting to the task's unlabeled data
(task-adaptive pretraining) improves performance even after domain-adaptive
pretraining. Finally, we show that adapting to a task corpus augmented using
simple data selection strategies is an effective alternative, especially when
resources for domain-adaptive pretraining might be unavailable. Overall, we
consistently find that multi-phase adaptive pretraining offers large gains in
task performance.

本研究通过研究不同领域和任务的分类模型，验证了在特定领域和任务上进行二次预训练（领域自适应和任务自适应预训练）可以显著提高性能，同时也发现多阶段适应预训练在任务表现上取得了大幅提升。