SIGMA: Bridging Structural and Distributional Gaps for Vision Foundation Model Adaptation 事件

SIGMA: Bridging Structural and Distributional Gaps for Vision Foundation Model Adaptation arXiv:2605.27893v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) have demonstrated impressive representational capabilities. However, adapting them to downstream tasks via full fine-tuning incurs prohibitive computational and storage overhead. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a compelling alternative, aiming to achieve performance parity with full fine-tuning at minimal