16 citations · 20 across the 9 of their papers we have counts for
9 papers
Compression Is Not Evaluation-Neutral: Fixed RAG Compression Can Distort Reader Comparisons
Sugam Panthi, Rabab Abdelfattah
Retrieval-augmented generation (RAG) evaluations often compare readers after a compressor has changed their evidence. This mixes two questions: which complete pipeline works best,…
Does it matter which Gaussians you pick in 4D Gaussian streaming?
Ashim Dahal, Rabab Abdelfattah, Nick Rahimi
Anchor-driven 4D Gaussian streaming methods such as Instant Gaussian Stream (IGS) update a dynamic scene each frame from a compact set of Gaussian anchors, chosen by default with F…
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Rabab Abdelfattah, Qing Guo, Xiaoguang Li +2
This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference.…
Leveraging Inpainting for Single-Image Shadow Removal
Xiaoguang Li, Qing Guo, Rabab Abdelfattah +4
Fully-supervised shadow removal methods achieve the best restoration qualities on public datasets but still generate some shadow remnants. One of the reasons is the lack of large-s…
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…
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…
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…
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…
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…