InspectVLM：理论上统一，实践中不可靠
InspectVLM: Unified in Theory, Unreliable in Practice
AI总结：
本文通过InspectVLM评估统一视觉语言模型在工业检测中的可行性，发现其在细粒度检测和鲁棒性上不及传统ResNet模型，表明当前VLM缺乏精度关键场景所需的视觉基础。
摘要：
统一视觉语言模型（VLM）承诺通过将分类、检测和关键点定位等多种视觉任务重新构建到单一语言驱动接口中，来简化计算机视觉流程。这种架构在工业检测中尤其具有吸引力，因为管理分散的任务特定模型会带来复杂性、低效性和维护开销。在本文中，我们使用InspectVLM（一个基于Florence-2的VLM，在InspectMM（我们新的大规模多模态、多任务检测数据集）上训练）批判性地评估了这一统一范式的可行性。虽然InspectVLM在图像级分类和结构化关键点任务上表现具有竞争力，但我们发现它在核心检测指标上无法匹配传统的基于ResNet的模型。值得注意的是，该模型在低提示变异性下表现出脆弱行为，对细粒度目标检测产生退化输出，并且经常无论视觉输入如何都默认为记忆化的语言响应。我们的研究结果表明，虽然语言驱动的统一提供了概念上的优雅性，但当前的VLM缺乏在精度关键的工业检测中部署所需的视觉基础和鲁棒性。
英文摘要：
Unified vision-language models (VLMs) promise to streamline computer vision pipelines by reframing multiple visual tasks such as classification, detection, and keypoint localization within a single language-driven interface. This architecture is particularly appealing in industrial inspection, where managing disjoint task-specific models introduces complexity, inefficiency, and maintenance overhead. In this paper, we critically evaluate the viability of this unified paradigm using InspectVLM, a Florence-2-based VLM trained on InspectMM, our new large-scale multimodal, multitask inspection dataset. While InspectVLM performs competitively on image-level classification and structured keypoint tasks, we find that it fails to match traditional ResNet-based models in core inspection metrics. Notably, the model exhibits brittle behavior under low prompt variability, produces degenerate outputs for fine-grained object detection, and frequently defaults to memorized language responses regardless of visual input. Our findings suggest that while language-driven unification offers conceptual elegance, current VLMs lack the visual grounding and robustness necessary for deployment in precision critical industrial inspections.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
