We propose a semi-supervised network for wide-angle portraits correction.
Wide-angle images often suffer from skew and distortion affected by perspective
distortion, especially noticeable at the face regions. Previous deep learning
based approaches need the ground-truth correction flow maps for training
guidance. However, such labels are expensive, which can only be obtained
manually. In this work, we design a semi-supervised scheme and build a
high-quality unlabeled dataset with rich scenarios, allowing us to
simultaneously use labeled and unlabeled data to improve performance.
Specifically, our semi-supervised scheme takes advantage of the consistency
mechanism, with several novel components such as direction and range
consistency (DRC) and regression consistency (RC). Furthermore, different from
the existing methods, we propose the Multi-Scale Swin-Unet (MS-Unet) based on
the multi-scale swin transformer block (MSTB), which can simultaneously learn
short-distance and long-distance information to avoid artifacts. Extensive
experiments demonstrate that the proposed method is superior to the
state-of-the-art methods and other representative baselines. The source code
and dataset are available at:
this https URL

提出一种半监督网络用于广角肖像矫正，利用一致性机制和多尺度 Swin Transformer 块构建 MS-Unet 网络，同时使用标记和未标记数据进行训练，实现了比现有方法更优异的结果。

多尺度变换器半监督广角人像校正

Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale  Transformer

With the development of underwater object grabbing technology, underwater
object recognition and segmentation of high accuracy has become a challenge.
The existing underwater object detection technology can only give the general
position of an object, unable to give more detailed information such as the
outline of the object, which seriously affects the grabbing efficiency. To
address this problem, we label and establish the first underwater semantic
segmentation dataset of real scene(DUT-USEG:DUT Underwater Segmentation
Dataset). The DUT- USEG dataset includes 6617 images, 1487 of which have
semantic segmentation and instance segmentation annotations, and the remaining
5130 images have object detection box annotations. Based on this dataset, we
propose a semi-supervised underwater semantic segmentation network focusing on
the boundaries(US-Net: Underwater Segmentation Network). By designing a pseudo
label generator and a boundary detection subnetwork, this network realizes the
fine learning of boundaries between underwater objects and background, and
improves the segmentation effect of boundary areas. Experiments show that the
proposed method improves by 6.7% in three categories of holothurian, echinus,
starfish in DUT-USEG dataset, and achieves state-of-the-art results. The DUT-
USEG dataset will be released at this https URL

为了提高水下抓取效率，我们标记并建立了第一个真实场景的水下语义分割数据集 (DUT-USEG:DUT Underwater Segmentation Dataset)，并基于此数据集提出了一个重点关注边界的半监督水下语义分割网络 (US-Net: Underwater Segmentation Network)，通过设计伪标签生成器和检测子网，实现了水下物体与背景之间边界的精细学习，并改善了边缘区域的分割效果，实验表明该方法在 DUT-USEG 数据集中的三类动物物种 (海葵，海胆和海星) 上提高了 6.7％的性能，达到了最先进的结果。