语义丰富性还是几何推理？VLM视觉不变性的脆弱性
Semantic Richness or Geometric Reasoning? The Fragility of VLM's Visual Invariance
AI总结：
研究揭示VLM在基本几何变换下的脆弱性，指出其在空间不变性与等变性上的不足，影响物体识别的可靠性。
摘要：
本文研究了当前最先进的视觉-语言模型（VLM）在基本几何变换下的根本性脆弱性。尽管现代VLM在语义任务如识别标准方向的对象和描述复杂场景方面表现出色，但它们在更基础的层面存在系统性失败：缺乏必要的空间不变性和等变性，以可靠地确定对象身份在简单旋转、缩放和恒等变换下的身份。我们通过在多样化的视觉领域（包括符号草图、自然照片和抽象艺术）中的系统评估来展示这一限制。当语义内容变得稀疏时，性能急剧下降，且这种行为在不同架构、模型容量和提示策略下均被观察到。总体而言，我们的结果揭示了当前VLM在语义理解和空间推理之间的系统性差距，突显了未来多模态系统中更强几何基础的必要性。
英文摘要：
This work investigates the fundamental fragility of state-of-the-art Vision-Language Models (VLMs) under basic geometric transformations. While modern VLMs excel at semantic tasks such as recognizing objects in canonical orientations and describing complex scenes, they exhibit systematic failures at a more fundamental level: lack of robust spatial invariance and equivariance required to reliably determine object identity under simple rotations, scaling, and identity transformations. We demonstrate this limitation through a systematic evaluation across diverse visual domains, including symbolic sketches, natural photographs, and abstract art. Performance drops sharply as semantic content becomes sparse, and this behavior is observed across architectures, model capacities, and prompting strategies. Overall, our results reveal a systematic gap between semantic understanding and spatial reasoning in current VLMs, highlighting the need for stronger geometric grounding in future multimodal systems.
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