评估开源视觉语言模型在物体描述任务中对领域偏移的鲁棒性
Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning
AI总结：
本文系统评估了开源视觉语言模型在单视图物体描述任务中面对物理领域偏移时的鲁棒性，发现所有受测模型在描述3D打印物体时性能显著下降，揭示了当前模型泛化能力的局限。
摘要：
视觉语言模型（VLMs）已成为从视觉数据生成文本描述的强大工具。尽管这些模型在网页级数据集上表现出色，但它们对许多实际应用中固有的领域偏移的鲁棒性仍未得到充分探索。本文针对 VLMs 在面临受控的物理领域偏移时，执行单视图物体描述任务的性能进行了系统评估。我们比较了两组不同物体集上的描述准确性：一组是多材质的真实世界工具，另一组是单材质的 3D 打印物品。3D 打印物品集在纹理和材料属性上引入了显著的领域偏移，挑战了模型的泛化能力。定量结果表明，与描述真实世界工具相比，所有受测 VLMs 在描述 3D 打印物体时均表现出显著的性能下降。这凸显了当前模型在超越表面特征进行泛化方面的关键局限，并强调了在真实世界信号处理应用中需要更鲁棒的架构。
英文摘要：
Vision-Language Models (VLMs) have emerged as powerful tools for generating textual descriptions from visual data. While these models excel on web-scale datasets, their robustness to the domain shifts inherent in many real-world applications remains under-explored. This paper presents a systematic evaluation of VLM performance on a single-view object captioning task when faced with a controlled, physical domain shift. We compare captioning accuracy across two distinct object sets: a collection of multi-material, real-world tools and a set of single-material, 3D-printed items. The 3D-printed set introduces a significant domain shift in texture and material properties, challenging the models' generalization capabilities. Our quantitative results demonstrate that all tested VLMs show a marked performance degradation when describing the 3D-printed objects compared to the real-world tools. This underscores a critical limitation in the ability of current models to generalize beyond surface-level features and highlights the need for more robust architectures for real-world signal processing applications.
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