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Achieving over 90% mAP in text-based person anomaly retrieval reveals the power of heterogeneous vision-language ensembles and selective multimodal reasoning.
Achieving a staggering 95.41% mAP@10, FaLCon revolutionizes text-based person anomaly search by combining global semantic matching with fine-grained verification to tackle the Sim2Real challenge.
Geometry-aware inference can significantly enhance the stability of traffic predictions without the need for retraining existing models.
