VisCoP：用于视觉语言模型视频域适应的视觉探测
VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models
AI总结：
研究针对视觉语言模型在新领域性能下降问题，提出参数高效的VisCoP框架，通过可学习视觉探测器增强视觉编码器，在多种适应设置下评估，该框架优于现有策略，能有效适应新领域且保留源域能力。
摘要：
大型视觉语言模型（VLM）在一般视觉推理方面表现出色，但在部署于与预训练数据有显著分布变化的新领域时，性能会大幅下降。现有域适应方法依赖微调标准VLM组件，但根据更新的组件不同，这些方法要么限制模型学习特定领域表示的能力，要么导致对先前获得能力的灾难性遗忘。我们引入了视觉上下文探测（VisCoP），这是一个参数高效的适应框架，用一组紧凑的可学习视觉探测器增强VLM视觉编码器。通过这些探测器学习特定领域的视觉表示，同时只需对预训练模型组件进行最小更新，VisCoP能有效适应新领域而不牺牲现有知识。我们在三种具有挑战性的适应设置下评估VisCoP：跨视图（从外心到自我中心）、跨模态（从RGB到深度）和跨任务（从人类理解到机器人控制）。在所有场景中，VisCoP始终优于现有域适应策略，在保持预训练VLM在源域能力的同时，实现了卓越的目标域性能。这些结果表明，轻量级视觉探测为在大幅分布变化下适应VLM提供了有效且强大的解决方案。代码、模型和评估协议可在该https网址获取。
英文摘要：
Large Vision Language Models (VLMs) excel at general visual reasoning but experience significant performance degradation when deployed in novel domains that exhibit substantial distribution shifts from their pretraining data. Existing domain adaptation methods rely on finetuning standard VLM components; however, depending on which components are updated, these approaches either limit the model's ability to learn domain-specific representations or cause catastrophic forgetting of previously acquired capabilities. We introduce Vision Contextualized Probing (VisCoP), a parameter-efficient adaptation framework that augments the VLM vision encoder with a compact set of learnable visual probes. By learning domain-specific visual representations through these probes while requiring only minimal updates to pretrained model components, VisCoP effectively adapts to new domains without sacrificing existing knowledge. We evaluate VisCoP across three challenging adaptation settings: cross-view (exocentric to egocentric), cross-modal (RGB to depth), and cross-task (human understanding to robot control). Across all scenarios, VisCoP consistently outperforms existing domain adaptation strategies, achieving superior target-domain performance while preserving the pretrained VLM's capabilities on the source domain. These results demonstrate that lightweight visual probing provides an effective and robust solution for adapting VLMs under substantial distribution shifts. Code, models, and evaluation protocols are available at https://github.com/dominickrei/VisCoP.
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