SemVink：通过视觉全局思维提升视觉语言模型对光学错觉的语义理解
SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking
AI总结：
针对视觉语言模型难以检测光学错觉中隐藏内容的问题，提出SemVink方法，通过将图像缩放到低分辨率消除噪声，在HC-Bench基准上实现超过99%的准确率，揭示了模型过度依赖高层语义的架构缺陷，倡导多尺度混合模型。
摘要：
视觉语言模型（VLMs）在语义任务中表现出色，但在一个核心的人类能力上却有所欠缺：通过缩放等感知调整来检测光学错觉或AI生成图像中的隐藏内容。我们引入了HC-Bench，一个包含112张具有隐藏文本、物体和错觉图像的基准测试，揭示了领先的视觉语言模型即使有明确的提示，其准确率也接近零（0-5.36%）。人类能本能地解决此类歧义，而视觉语言模型却因过度依赖高层语义而失败。引人注目的是，我们提出了SemVink（语义视觉思维），仅通过将图像缩放到低分辨率（32-128像素），即可消除冗余视觉噪声，从而解锁超过99%的准确率。这暴露了一个关键的架构缺陷：视觉语言模型优先考虑抽象推理，而忽视了对于现实世界鲁棒性至关重要的低级视觉操作。我们的工作呼吁转向集成多尺度处理的混合模型，弥合计算视觉与人类认知之间的差距，以应用于医学成像、安全及其他领域。
英文摘要：
Vision-language models (VLMs) excel in semantic tasks but falter at a core human capability: detecting hidden content in optical illusions or AI-generated images through perceptual adjustments like zooming. We introduce HC-Bench, a benchmark of 112 images with hidden text, objects, and illusions, revealing that leading VLMs achieve near-zero accuracy (0-5.36%)-even with explicit prompting. Humans resolve such ambiguities instinctively, yet VLMs fail due to an overreliance on high-level semantics. Strikingly, we propose SemVink (Semantic Visual Thinking) by simply scaling images to low resolutions (32-128 pixels), which unlocks >99% accuracy by eliminating redundant visual noise. This exposes a critical architectural flaw: VLMs prioritize abstract reasoning over low-level visual operations crucial for real-world robustness. Our work urges a shift toward hybrid models integrating multi-scale processing, bridging the gap between computational vision and human cognition for applications in medical imaging, security, and beyond.
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。
