A decoupling strategy optimizes object slots for holistic class identity during training and composes them at inference to achieve better generalization to unseen concepts in continual few-shot settings.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=
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Unlocking Compositional Generalization in Continual Few-Shot Learning
A decoupling strategy optimizes object slots for holistic class identity during training and composes them at inference to achieve better generalization to unseen concepts in continual few-shot settings.