5 papers
LARE: Low-Attention Region Encoding for Text-Image Retrieval
Abdulmalik Alquwayfili, Faisal Almeshal, Jumanah Almajnouni +8
Image retrieval in crowded scenes is particularly challenging due to the salience bias of conventional visual encoders, which tend to focus on dominant objects while neglecting low…
Look Beyond Saliency: Low-Attention Guided Dual Encoding for Video Semantic Search
Faisal Aljehrai, Mohammed A. Alkhrashi, Alreem Almuhrij +6
Video semantic search in densely crowded scenes remains a challenging task due to visual encoders tendency to prioritize salient foreground regions while neglecting contextually im…
VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis
Fatima AlGhamdi, Omar Alharbi, Abdullah Aldwyish +3
Detecting anomalies in crowded scenes is challenging due to severe inter-person occlusions and highly dynamic, context-dependent motion patterns. Existing approaches often struggle…
On Robust Learning from Noisy Labels: A Permutation Layer Approach
Salman Alsubaihi, Mohammed Alkhrashi, Raied Aljadaany +2
The existence of label noise imposes significant challenges (e.g., poor generalization) on the training process of deep neural networks (DNN). As a remedy, this paper introduces a…
Proximal Splitting Networks for Image Restoration
Raied Aljadaany, Dipan K. Pal, Marios Savvides
Image restoration problems are typically ill-posed requiring the design of suitable priors. These priors are typically hand-designed and are fully instantiated throughout the proce…