Recent advancements in computer vision predominantly rely on learning-based
systems, leveraging annotations as the driving force to develop specialized
models. However, annotating pixel-level information, particularly in semantic
segmentation, presents a challenging and labor-intensive task, prompting the
need for autonomous processes. In this work, we propose GranSAM which
distinguishes itself by providing semantic segmentation at the user-defined
granularity level on unlabeled data without the need for any manual
supervision, offering a unique contribution in the realm of semantic mask
annotation method. Specifically, we propose an approach to enable the Segment
Anything Model (SAM) with semantic recognition capability to generate
pixel-level annotations for images without any manual supervision. For this, we
accumulate semantic information from synthetic images generated by the Stable
Diffusion model or web crawled images and employ this data to learn a mapping
function between SAM mask embeddings and object class labels. As a result, SAM,
enabled with granularity-adjusted mask recognition, can be used for pixel-level
semantic annotation purposes. We conducted experiments on the PASCAL VOC 2012
and COCO-80 datasets and observed a +17.95% and +5.17% increase in mIoU,
respectively, compared to existing state-of-the-art methods when evaluated
under our problem setting.

提供了一种在未标记数据上进行用户定义粒度层次的语义分割的方法，通过累积全局语义信息并学习映射函数，使得带有粒度调整的语义识别的 Segment Anything Model（SAM）能够用于像素级语义注释，实验结果在 PASCAL VOC 2012 和 COCO-80 数据集上分别比现有最先进方法的 mIoU 提高了 17.95% 和 5.17%。

面向粒度调整的像素级语义标注

Towards Granularity-adjusted Pixel-level Semantic Annotation

In city, information about urban objects such as water supply, railway lines,
power lines, buildings, roads, etc., is necessary for city planning. In
particular, information about the spread of these objects, locations and
capacity is needed for the policymakers to make impactful decisions. This
thesis aims to segment the building and roads from the aerial image captured by
the satellites and UAVs. Many different architectures have been proposed for
the semantic segmentation task and UNet being one of them. In this thesis, we
propose a novel architecture based on Google's newly proposed EfficientNetV2 as
an encoder for feature extraction with UNet decoder for constructing the
segmentation map. Using this approach we achieved a benchmark score for the
Massachusetts Building and Road dataset with an mIOU of 0.8365 and 0.9153
respectively.

通过使用谷歌的新模型，本文旨在对卫星和无人机航拍图像进行城市目标（建筑和道路）的语义分割，以提供城市规划决策所需的准确信息。

使用 EffUNet 和迁移学习方法进行建筑物和道路分割

Building and Road Segmentation Using EffUNet and Transfer Learning  Approach

Semantic segmentation is a challenging vision problem that usually
necessitates the collection of large amounts of finely annotated data, which is
often quite expensive to obtain. Coarsely annotated data provides an
interesting alternative as it is usually substantially more cheap. In this
work, we present a method to leverage coarsely annotated data along with fine
supervision to produce better segmentation results than would be obtained when
training using only the fine data. We validate our approach by simulating a
scarce data setting with less than 200 low resolution images from the
Cityscapes dataset and show that our method substantially outperforms solely
training on the fine annotation data by an average of 15.52% mIoU and
outperforms the coarse mask by an average of 5.28% mIoU.

本文提出了一种方法，利用粗略标注的数据和精细监督来产生比仅使用精细数据训练时更好的分割结果。在 Cityscapes 数据集上进行实验，验证了该方法的有效性。