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Randomly permuting samples reveals that existing TMC methods may be misleadingly reliant on privileged sample arrangements, leading to severe performance drops.
Achieve stable and efficient online IMTS forecasting by calibrating a frozen source model with a lightweight, uncertainty-steered dual-expert system.
By warm-starting a dynamic priority queue with seen and generated unseen visual prototypes, this CZSL method significantly mitigates distribution shift at test time, outperforming existing approaches.