Panoptic segmentation is an important computer vision task which combines
semantic and instance segmentation. It plays a crucial role in domains of
medical image analysis, self-driving vehicles, and robotics by providing a
comprehensive understanding of visual environments. Traditionally, deep
learning panoptic segmentation models have relied on dense and accurately
annotated training data, which is expensive and time consuming to obtain.
Recent advancements in self-supervised learning approaches have shown great
potential in leveraging synthetic and unlabelled data to generate pseudo-labels
using self-training to improve the performance of instance and semantic
segmentation models. The three available methods for self-supervised panoptic
segmentation use proposal-based transformer architectures which are
computationally expensive, complicated and engineered for specific tasks. The
aim of this work is to develop a framework to perform embedding-based
self-supervised panoptic segmentation using self-training in a
synthetic-to-real domain adaptation problem setting.

通过自学习方法在合成到真实的领域适应问题设置中，开发了一个基于嵌入的自学习全景分割框架。

自训练全景分割

Self-trained Panoptic Segmentation

Deep learning-based scene text detection can achieve preferable performance,
powered with sufficient labeled training data. However, manual labeling is time
consuming and laborious. At the extreme, the corresponding annotated data are
unavailable. Exploiting synthetic data is a very promising solution except for
domain distribution mismatches between synthetic datasets and real datasets. To
address the severe domain distribution mismatch, we propose a synthetic-to-real
domain adaptation method for scene text detection, which transfers knowledge
from synthetic data (source domain) to real data (target domain). In this
paper, a text self-training (TST) method and adversarial text instance
alignment (ATA) for domain adaptive scene text detection are introduced. ATA
helps the network learn domain-invariant features by training a domain
classifier in an adversarial manner. TST diminishes the adverse effects of
false positives~(FPs) and false negatives~(FNs) from inaccurate pseudo-labels.
Two components have positive effects on improving the performance of scene text
detectors when adapting from synthetic-to-real scenes. We evaluate the proposed
method by transferring from SynthText, VISD to ICDAR2015, ICDAR2013. The
results demonstrate the effectiveness of the proposed method with up to 10%
improvement, which has important exploration significance for domain adaptive
scene text detection. Code is available at
this https URL

研究了合成数据对实际数据的转化问题，并提出了基于对抗文本实例对齐与文本自训练的技术来解决领域差异，以提高场景文本检测的性能。