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# Adapter-based Fine-Tuning

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  1. arXiv

    Federated fine-tuning stops choosing sides: shared adapters plus a local vision projection hit 82.15 harmonic mean across ten datasets

    BLEND splits federated CLIP fine-tuning into shared vision and text adapters plus a per-client vision projection adapter, reaching an average harmonic mean of 82.15 across ten image datasets, 4.81 points above the strongest baseline.

    Zhejiang UniversityUniversity at Buffalo, State University of New York
    Payam Abdisarabshali（First author）
    BLENDFederated LearningVision-Language ModelsAdapter-based Fine-TuningContrastive Language-Image Pretraining
  2. Visual Computing for Industry Biomedicine and Art

    Lesion segmentation drops the per-lesion box: one concept prompt lets SAM3 outline 11 lesion types across 13 datasets

    SAM3 segments lesions from text or image-exemplar concept prompts across 13 datasets and 11 lesion types, beating SAM2 on Dice in 10 datasets, by 4.41 on ISLES22 and 5.39 on ISIC2018.

    University of PennsylvaniaSouthwestern Medical CenterThe University of Texas Southwestern Medical Center
    Guoping Xu（First author）You Zhang（Corresponding author）Ying Zhang（Corresponding author）
    SAM3Lesion SegmentationAdapter-based Fine-TuningConcept-Prompt-Based Segmentation

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