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Region purification in MLTTA reveals reliable cues that dramatically enhance multi-label recognition performance, achieving a 4.05% mAP boost over existing methods.
Adapting pretrained models on-the-fly to ever-changing data distributions could redefine how we deploy AI in dynamic environments.
