Reasoning-intensive retrieval is fundamentally challenged when queries demand deep semantic inference and arrive in multimodal form. Existing systems treat each query in isolation, rely on monolithic rerankers, and lack retrievers trained for cross-modal reasoning. We introduce MEMORA, a Memory-Enhanced Multimodal Orch...
The intent is to bridge the historically separate BEV-segmentation and 3D-occupancy literatures within a single taxonomy and identify open problems in label efficiency, robustness, temporal forecasting, and deployment.
Abdelrahman S. Heikal, Mostafa Farouk Senussi, Ahmed Salem et al.· Mathematics· 0 citations
This study proposes ConvNeXtLSD, a DL-based multi-class classification framework for automated cattle LSD diagnosis that achieves superior classification performance and maintains practical computational efficiency, demonstrating an effective balance between predictive performance and computational cost for real-world...
Mostafa Farouk Senussi, Abdelrahman S. Heikal, Asmaa Gamal Abdelbasset et al.· Scientific Reports· 0 citations
Under fully reproducible conditions on UCF-Crime and XD-Violence, SETAS-VAD achieves state-of-the-art temporal localization, with per-threshold gains increasing at stricter IoU values, indicating improved boundary precision rather than coarse detection sensitivity.
