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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.
Achieve high-fidelity image enhancement on mobile devices even after quantization by training a model that anticipates and adapts to low-precision representations.
