  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.

Computer Vision and Pattern Recognition