20 citations · 20 across the 7 of their papers we have counts for
10 papers
Your Benchmark Is Not Saturated: Reviving Multiple-Choice Evaluation with Answer Pooling
Mohamed Eltahir, Abobaker Ahmed, Nawaf Barebood +3
Multiple-choice benchmarks are cheap to grade and are running out of room, and the standard remedy, writing harder items, is slow and repeated for every benchmark. A saturated benc…
AnswerMap: Faithful Spatial Interpretability of VLMs from Answer Posteriors
Mohamed Eltahir, Fardows Adam, Duaa M. Tahir +6
When a VLM answers a visual query, current interpretability tools rely on text rationales, which use a mismatched modality, or on internal read-outs, which originate too early to r…
The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading
Muath Alyobi, Mohamed Eltahir, Almoayyad Abuljdail +3
Language models often process long inputs sequentially in chunks, but continuing to read after sufficient evidence has been acquired wastes computation. Existing stopping mechanism…
Generative Late-Interaction Embeddings For Visual Document Retrieval
Mohamed Eltahir, Talal Aloushan, Rose Khairoalsendi +5
Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storage. Existing compression methods retain a subset or local averag…
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…
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…
AutoArabic: A Three-Stage Framework for Localizing Video-Text Retrieval Benchmarks
Mohamed Eltahir, Osamah Sarraj, Abdulrahman Alfrihidi +4
Video-to-text and text-to-video retrieval are dominated by English benchmarks (e.g. DiDeMo, MSR-VTT) and recent multilingual corpora (e.g. RUDDER), yet Arabic remains underserved,…
Multimodal Lengthy Videos Retrieval Framework and Evaluation Metric
Mohamed Eltahir, Osamah Sarraj, Mohammed Bremoo +5
Precise video retrieval requires multi-modal correlations to handle unseen vocabulary and scenes, becoming more complex for lengthy videos where models must perform effectively wit…
AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions
Chenghao Qian, Mahdi Rezaei, Saeed Anwar +4
Adverse conditions like snow, rain, nighttime, and fog, pose challenges for autonomous driving perception systems. Existing methods have limited effectiveness in improving essentia…