Two challenges are presented when parsing road scenes in UAV images. First,
the high resolution of UAV images makes processing difficult. Second,
supervised deep learning methods require a large amount of manual annotations
to train robust and accurate models. In this paper, an unsupervised road
parsing framework that leverages recent advances in vision language models and
fundamental computer vision model is introduced.Initially, a vision language
model is employed to efficiently process ultra-large resolution UAV images to
quickly detect road regions of interest in the images. Subsequently, the vision
foundation model SAM is utilized to generate masks for the road regions without
category information. Following that, a self-supervised representation learning
network extracts feature representations from all masked regions. Finally, an
unsupervised clustering algorithm is applied to cluster these feature
representations and assign IDs to each cluster. The masked regions are combined
with the corresponding IDs to generate initial pseudo-labels, which initiate an
iterative self-training process for regular semantic segmentation. The proposed
method achieves an impressive 89.96% mIoU on the development dataset without
relying on any manual annotation. Particularly noteworthy is the extraordinary
flexibility of the proposed method, which even goes beyond the limitations of
human-defined categories and is able to acquire knowledge of new categories
from the dataset itself.

通过使用最新的视觉语言模型和基本的计算机视觉模型，本研究介绍了一种无监督道路解析框架，用于处理超高分辨率的无人机图像，并且无需任何手动标注即可在开发数据集上达到 89.96％的 mIoU。

无监督高分辨率无人机影像的道路场景解析

Unsupervised semantic segmentation of high-resolution UAV imagery for  road scene parsing

This work presents a graph neural network (GNN) framework for solving the
maximum independent set (MIS) problem, inspired by dynamic programming (DP).
Specifically, given a graph, we propose a DP-like recursive algorithm based on
GNNs that firstly constructs two smaller sub-graphs, predicts the one with the
larger MIS, and then uses it in the next recursive call. To train our
algorithm, we require annotated comparisons of different graphs concerning
their MIS size. Annotating the comparisons with the output of our algorithm
leads to a self-training process that results in more accurate self-annotation
of the comparisons and vice versa. We provide numerical evidence showing the
superiority of our method vs prior methods in multiple synthetic and real-world
datasets.

本文提出了一种基于动态规划的图神经网络（GNN）框架来解决最大独立集（MIS）问题，通过递归算法构建子图并预测具有较大 MIS 的子图，进而在下一个递归调用中使用。我们通过对不同图形之间的 MIS 大小进行注释比较来训练算法，注释比较的输出结果用于自我训练过程，以提高注释比较的准确性。我们提供了在多个合成和真实数据集中证明我们算法优越性的数值证据。