no code implementations • ICCV 2023 • Rabab Abdelfattah, Qing Guo, Xiaoguang Li, XiaoFeng Wang, Song Wang
Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels.
1 code implementation • ICCV 2023 • Xiaoguang Li, Qing Guo, Rabab Abdelfattah, Di Lin, Wei Feng, Ivor Tsang, Song Wang
In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.
no code implementations • 24 Oct 2022 • Xin Zhang, Rabab Abdelfattah, Yuqi Song, XiaoFeng Wang
Through comprehensive experiments on three large-scale multi-label image datasets, i. e. MS-COCO, NUS-WIDE, and Pascal VOC12, we show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches in most cases, and in some cases even approaches with fully labeled datasets.
no code implementations • 24 Oct 2022 • Xin Zhang, Rabab Abdelfattah, Yuqi Song, Samuel A. Dauchert, XiaoFeng Wang
Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications.
no code implementations • 20 Oct 2022 • Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, XiaoFeng Wang, Song Wang
To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
Multi-Label Classification Multi-Label Image Classification +2
no code implementations • 22 Aug 2022 • Rabab Abdelfattah, Xin Zhang, Zhenyao Wu, Xinyi Wu, XiaoFeng Wang, Song Wang
A special case is to annotate only one positive label in each training image.
Multi-Label Classification Multi-Label Image Classification +1
no code implementations • 14 Apr 2022 • Rabab Abdelfattah, XiaoFeng Wang, Song Wang
Accurate segmentation of power lines in various aerial images is very important for UAV flight safety.
1 code implementation • 20 Oct 2020 • 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 safety.