8 papers
HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting
Reem AlYabis, Fares AlTuwaim, AlJawharh AlOtaibi +1
Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras o…
When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah
AlJawharh S. AlOtaibi, Mohamed Eltahir, Jude AlSubaie
Learning from few labeled examples is a central challenge in tabular machine learning, and it becomes the binding constraint in domains where labeling is costly, such as crowd moni…
GridProbe: Posterior-Probing for Adaptive Test-Time Compute in Long-Video VLMs
Mohamed Eltahir, Lama Ayash, Ali Habibullah +2
Long-video understanding in VLMs is bottlenecked by a single monolithic forward pass over thousands of frames at quadratic attention cost. A common mitigation is to first select a…
GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids
Mohamed Eltahir, Ahmed O. Ibrahim, Obada Siralkhatim +2
Vision-Language Models (VLMs) are powerful open-set reasoners, yet their direct use as anomaly detectors in video surveillance is fragile: without calibrated anomaly priors, they a…
VideoAtlas: Navigating Long-Form Video in Logarithmic Compute
Mohamed Eltahir, Ali Habibullah, Yazan Alshoibi +3
Extending language models to video introduces two challenges: representation, where existing methods rely on lossy approximations, and long-context, where caption- or agent-based p…
Vote-in-Context: Turning VLMs into Zero-Shot Rank Fusers
Mohamed Eltahir, Ali Habibullah, Lama Ayash +2
In the retrieval domain, candidates' fusion from heterogeneous retrievers is a long-standing challenge, particularly for complex, multi-modal data such as videos. While typical fus…