most citedPLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2022

An Effective Approach for Multi-label Classification with Missing Labels

Xin Zhang, Rabab Abdelfattah, Yuqi Song +1

Compared with multi-class classification, multi-label classification that contains more than one class is more suitable in real life scenarios. Obtaining fully labeled high-quality…

cs.CV2022

Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image

Xin Zhang, Rabab Abdelfattah, Yuqi Song +2

Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications. Promptly obtaining accurate and efficient…

cs.CV2022

G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification

Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda +2

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in pract…

cs.CV20221 cited

PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images

Rabab Abdelfattah, Xiaofeng Wang, Song Wang

Accurate segmentation of power lines in various aerial images is very important for UAV flight safety. The complex background and very thin structures of power lines, however, make…

cs.CV2020

TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines

Rabab Abdelfattah, Xiaofeng Wang, Song Wang

Accurate detection and segmentation of transmission towers~(TTs) and power lines~(PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV sa…