20 citations · 30 across the 6 of their papers we have counts for
6 papers
Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction
Ali Jamali, Swalpa Kumar Roy, Jonathan Li +1
In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic. Although deep CNNs have presented e…
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot
Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos +4
Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentat…
Dynamic Clustering Transformer Network for Point Cloud Segmentation
Dening Lu, Jun Zhou, Kyle Yilin Gao +4
Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulte…
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
Diogo Nunes Goncalves, Jose Marcato Junior, Pedro Zamboni +4
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with indepe…
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
Dening Lu, Kyle Gao, Qian Xie +2
Although the application of Transformers in 3D point cloud processing has achieved significant progress and success, it is still challenging for existing 3D Transformer methods to…
Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
Yan Liu, Qingyong Hu, Yinjie Lei +3
Learning dense point-wise semantics from unstructured 3D point clouds with fewer labels, although a realistic problem, has been under-explored in literature. While existing weakly…