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  • Prompt-based Adaptation in Large-scale Vision Models: A Survey
    In this survey, we revisit the designs of VP and VPT from first principles and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)
  • Prompt-based Adaptation in Large-scale Vision Models: A Survey
    This survey revisits the designs of VP and VPT from first principles and conceptualizes them within a unified framework termed Prompt-based Adaptation (PA), which is the first comprehensive survey dedicated to PA methodologies and applications in light of their distinct characteristics
  • Prompt-based Adaptation in Large-scale Vision Models: A Survey
    In this survey, we revisit the designs of VP and VPT from first principles, and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)
  • Prompt-based Adaptation in Large-scale Vision Models: A Survey
    Prompt-Based Adaptation (PA) introduces small prompt parameters while freezing the backbone, efficiently steering pretrained models to new tasks This survey provides the first comprehensive and unified overview of PA in large vision models
  • Prompt-based Adaptation in Large-scale Vision Models: A Survey
    In this survey, we revisit the designs of VP and VPT from first principles, and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)
  • Prompt-based Adaptation in Large-scale Vision Models: A . . .
    In this survey, we revisit the designs of VP and VPT from first principles, and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)
  • Prompt-based Adaptation in Large-scale Vision Models: A Survey | alphaXiv
    In this survey, we revisit the designs of VP and VPT from first principles, and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)
  • Prompt-based Adaptation in Large-scale Vision Models: A . . .
    In this survey, we revisit the designs of VP and VPT from first principles, and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)
  • arxiv每日论文-计算机视觉2025-10-16 - 知乎
    摘要：大型视觉语言模型（VLMs）在通用视觉推理任务中表现出色，但当应用于与预训练数据分布有显著差异的新领域时，性能会急剧下降。 现有的领域自适应方法会对不同的VLM组件进行微调，但这通常会导致特定领域特征学习有限，或出现对先前能力的灾难性遗忘。 为解决这些问题，我们提出了视觉上下文探测（VisCoP）方法，该方法通过一组紧凑的可学习视觉探针来增强VLM的视觉编码器。 这些探针能够在对预训练参数进行最小修改的情况下，实现高效的特定领域自适应。 我们在三种具有挑战性的领域自适应场景下对VisCoP进行了评估，包括跨视角（从外中心视角到第一人称视角）、跨模态（从RGB到深度）和跨任务（从人类理解到机器人控制）。
  • Prompt-based Adaptation in Large-scale Vision Models: A Survey
    In this survey, we revisit the designs of VP and VPT from first principles, and conceptualize them within a unified framework termed Prompt-based Adaptation (PA)





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