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...
Oblique retrieval, as exemplified by OBLIQ-Bench, asks a retriever to find documents whose relevance is determined by a latent attribute (an implicit stance, an analogous reasoning technique, an authorial fingerprint, or a vague tip-of-the-tongue recollection) that has little or no surface expression in the document. S...
Mahmoud Abdalla, Abdelrahman Abdallah, Shaimaa Sedek et al.· 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.
A lightweight inference-time correction that estimates and removes a popularity prior from model predictions is proposed, which requires no fine-tuning, is label-free at test time, and adds negligible computational cost.
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani et al.· 0 citations
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