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University of Science
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VietFashion reveals that current AI models struggle to capture the intricate cultural semantics of traditional garments, exposing significant gaps in fine-grained retrieval capabilities.
Deepfake detectors can be made far more robust to real-world image corruptions by training on heavily degraded data and ensembling complementary feature streams.
Naive nearest neighbor retrieval cripples tabular in-context learning for clinical risk prediction, but task-aligned retrieval unlocks significant gains, especially when data is messy.
EVENT-Retriever demonstrates that combining language-based reasoning with multimodal retrieval significantly improves performance in complex, real-world image understanding tasks, achieving top-1 score in the EVENTA 2025 Grand Challenge.
