Amidst the swift advancements in photography and sensor technologies,
high-definition cameras have become commonplace in the deployment of Unmanned
Aerial Vehicles (UAVs) for diverse operational purposes. Within the domain of
UAV imagery analysis, the segmentation of ultra-high resolution images emerges
as a substantial and intricate challenge, especially when grappling with the
constraints imposed by GPU memory-restricted computational devices. This paper
delves into the intricate problem of achieving efficient and effective
segmentation of ultra-high resolution UAV imagery, while operating under
stringent GPU memory limitation. The strategy of existing approaches is to
downscale the images to achieve computationally efficient segmentation.
However, this strategy tends to overlook smaller, thinner, and curvilinear
regions. To address this problem, we propose a GPU memory-efficient and
effective framework for local inference without accessing the context beyond
local patches. In particular, we introduce a novel spatial-guided
high-resolution query module, which predicts pixel-wise segmentation results
with high quality only by querying nearest latent embeddings with the guidance
of high-resolution information. Additionally, we present an efficient
memory-based interaction scheme to correct potential semantic bias of the
underlying high-resolution information by associating cross-image contextual
semantics. For evaluation of our approach, we perform comprehensive experiments
over public benchmarks and achieve superior performance under both conditions
of small and large GPU memory usage limitations. We will release the model and
codes in the future.

该论文阐述了在 GPU 内存限制的情况下，实现高效而有效的超高分辨率无人机图像分割的复杂问题，提出了一种新颖的空间引导式高分辨率查询模块和高效的基于内存的交互方案来改进现有方法中较小、较细和曲线区域的分割效果。

基于内存限制的超高分辨率无人机影像语义分割

Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV  Imagery

Segmentation of ultra-high resolution images is increasingly demanded, yet
poses significant challenges for algorithm efficiency, in particular
considering the (GPU) memory limits. Current approaches either downsample an
ultra-high resolution image or crop it into small patches for separate
processing. In either way, the loss of local fine details or global contextual
information results in limited segmentation accuracy. We propose collaborative
Global-Local Networks (GLNet) to effectively preserve both global and local
information in a highly memory-efficient manner. GLNet is composed of a global
branch and a local branch, taking the downsampled entire image and its cropped
local patches as respective inputs. For segmentation, GLNet deeply fuses
feature maps from two branches, capturing both the high-resolution fine
structures from zoomed-in local patches and the contextual dependency from the
downsampled input. To further resolve the potential class imbalance problem
between background and foreground regions, we present a coarse-to-fine variant
of GLNet, also being memory-efficient. Extensive experiments and analyses have
been performed on three real-world ultra-high aerial and medical image datasets
(resolution up to 30 million pixels). With only one single 1080Ti GPU and less
than 2GB memory used, our GLNet yields high-quality segmentation results and
achieves much more competitive accuracy-memory usage trade-offs compared to
state-of-the-arts.

提出了一种以极高的内存效率保留全局和局部信息的协作全局 - 本地网络，同样适用于粗到细的分割，用于超高分辨率图像的分割。