The present work discusses the use of a weakly-supervised deep learning
algorithm that reduces the cost of labelling pixel-level masks for complex
radio galaxies with multiple components. The algorithm is trained on weak
class-level labels of radio galaxies to get class activation maps (CAMs). The
CAMs are further refined using an inter-pixel relations network (IRNet) to get
instance segmentation masks over radio galaxies and the positions of their
infrared hosts. We use data from the Australian Square Kilometre Array
Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe
(EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS
sensitivity of 25-35 $\mu$Jy/beam. We demonstrate that weakly-supervised deep
learning algorithms can achieve high accuracy in predicting pixel-level
information, including masks for the extended radio emission encapsulating all
galaxy components and the positions of the infrared host galaxies. We evaluate
the performance of our method using mean Average Precision (mAP) across
multiple classes at a standard intersection over union (IoU) threshold of 0.5.
We show that the model achieves a mAP$_{50}$ of 67.5\% and 76.8\% for radio
masks and infrared host positions, respectively. The network architecture can
be found at the following link: this https URL

讨论使用弱监督深度学习算法，以减少复杂射电星系的像素级标签成本。使用澳大利亚平方公里阵列（ASKAP）望远镜的数据，展示了该算法在预测像素级信息方面的高准确性，包括射电辐射的掩蔽遥远星系中的所有成分和红外主机位置的掩模。

利用弱标签进行广义无线电星系形态学识别的深度学习

Deep Learning for Morphological Identification of Extended Radio  Galaxies using Weak Labels

Given a natural language expression and an image/video, the goal of referring
segmentation is to produce the pixel-level masks of the entities described by
the subject of the expression. Previous approaches tackle this problem by
implicit feature interaction and fusion between visual and linguistic
modalities in a one-stage manner. However, human tends to solve the referring
problem in a progressive manner based on informative words in the expression,
i.e., first roughly locating candidate entities and then distinguishing the
target one. In this paper, we propose a Cross-Modal Progressive Comprehension
(CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I
(Image) module and a CMPC-V (Video) module to improve referring image and video
segmentation models. For image data, our CMPC-I module first employs entity and
attribute words to perceive all the related entities that might be considered
by the expression. Then, the relational words are adopted to highlight the
target entity as well as suppress other irrelevant ones by spatial graph
reasoning. For video data, our CMPC-V module further exploits action words
based on CMPC-I to highlight the correct entity matched with the action cues by
temporal graph reasoning. In addition to the CMPC, we also introduce a simple
yet effective Text-Guided Feature Exchange (TGFE) module to integrate the
reasoned multimodal features corresponding to different levels in the visual
backbone under the guidance of textual information. In this way, multi-level
features can communicate with each other and be mutually refined based on the
textual context. Combining CMPC-I or CMPC-V with TGFE can form our image or
video version referring segmentation frameworks and our frameworks achieve new
state-of-the-art performances on four referring image segmentation benchmarks
and three referring video segmentation benchmarks respectively.

本文提出了一个跨模态的渐进式理解方案，分别应用于图像和视频的参考分割中，通过使用实体，属性和关系词汇进行空间图形推理和时间图形推理来解决语言表达的主语并输出像素级掩码。

跨模态逐步理解用于区域引用的分割

Cross-Modal Progressive Comprehension for Referring Segmentation

Learning segmentation from synthetic data and adapting to real data can
significantly relieve human efforts in labelling pixel-level masks. A key
challenge of this task is how to alleviate the data distribution discrepancy
between the source and target domains, i.e. reducing domain shift. The common
approach to this problem is to minimize the discrepancy between feature
distributions from different domains through adversarial training. However,
directly aligning the feature distribution globally cannot guarantee
consistency from a local view (i.e. semantic-level), which prevents certain
semantic knowledge learned on the source domain from being applied to the
target domain. To tackle this issue, we propose a semi-supervised approach
named Alleviating Semantic-level Shift (ASS), which can successfully promote
the distribution consistency from both global and local views. Specifically,
leveraging a small number of labeled data from the target domain, we directly
extract semantic-level feature representations from both the source and the
target domains by averaging the features corresponding to same categories
advised by pixel-level masks. We then feed the produced features to the
discriminator to conduct semantic-level adversarial learning, which
collaborates with the adversarial learning from the global view to better
alleviate the domain shift. We apply our ASS to two domain adaptation tasks,
from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments
demonstrate that: (1) ASS can significantly outperform the current unsupervised
state-of-the-arts by employing a small number of annotated samples from the
target domain; (2) ASS can beat the oracle model trained on the whole target
dataset by over 3 points by augmenting the synthetic source data with annotated
samples from the target domain without suffering from the prevalent problem of
overfitting to the source domain.

本文提出了一种半监督的方法 ASS 来处理域自适应问题，使用语义级别的对抗学习来提高全局和局部特征的分布一致性，从而更好地减轻源域和目标域之间的数据分布差异。实验结果表明，使用少量的注释样本，ASS 可以显著优于目前的无监督方法，并可以通过增加目标域的注释样本克服针对源域的过度拟合.

减轻语义级别转移：一种用于语义分割的半监督域自适应方法

Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation  Method for Semantic Segmentation

Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g.
human parsing, remains a challenging task. The ambiguous boundary between
different semantic parts and those categories with similar appearance usually
are confusing, leading to unexpected noises in ground truth masks. To tackle
the problem of learning with label noises, this work introduces a purification
strategy, called Self-Correction for Human Parsing (SCHP), to progressively
promote the reliability of the supervised labels as well as the learned models.
In particular, starting from a model trained with inaccurate annotations as
initialization, we design a cyclically learning scheduler to infer more
reliable pseudo-masks by iteratively aggregating the current learned model with
the former optimal one in an online manner. Besides, those correspondingly
corrected labels can in turn to further boost the model performance. In this
way, the models and the labels will reciprocally become more robust and
accurate during the self-correction learning cycles. Benefiting from the
superiority of SCHP, we achieve the best performance on two popular
single-person human parsing benchmarks, including LIP and Pascal-Person-Part
datasets. Our overall system ranks 1st in CVPR2019 LIP Challenge. Code is
available at this https URL

该研究使用了自校准策略（SCHP）来提高人类解析标签的可靠性，从而更好地训练模型并取得 LIP 挑战的第一名。