标题： 视觉字谜揭示了不同视觉模型整体形状处理中的隐藏差异 显示英文标题

标题： Visual Anagrams Reveal Hidden Differences in Holistic Shape Processing Across Vision Models

摘要： 人类能够基于局部纹理线索和物体部分的配置来识别物体，然而当代视觉模型主要提取局部纹理线索，导致特征脆弱且非组合性。 形状与纹理偏差的研究将形状和纹理表示对立起来，以形状相对于纹理来衡量，忽略了模型（和人类）可以同时依赖这两种线索的可能性，并掩盖了这两种表示的绝对质量。 因此，我们将形状评估重新定义为绝对配置能力的问题，通过配置形状得分（CSS）来实现，该得分（i）衡量在保留局部纹理的同时，通过置换全局部分排列来描绘不同物体类别的物体混写对中识别图像的能力。 在86个卷积、Transformer和混合模型中，CSS（ii）揭示了广泛的配置敏感性，完全自监督和语言对齐的Transformer——如DINOv2、SigLIP2和EVA-CLIP——占据了CSS谱系的顶端。 机制探测显示，（iii）高CSS网络依赖于长距离交互：半径控制的关注掩码会消除性能，显示出独特的U型整合轮廓，并且表征相似性分析揭示了从局部到全局编码的中深度过渡。 BagNet对照组仍处于随机水平（iv），排除了“边界黑客”策略。 最后，（v）我们展示了配置形状得分也能预测其他依赖形状的评估。 总体而言，我们认为通向真正稳健、可泛化和类人视觉系统之路可能不在于强迫在形状和纹理之间做出人工选择，而在于能够无缝整合局部纹理和全局配置形状的架构和学习框架。 显示英文摘要

摘要： Humans are able to recognize objects based on both local texture cues and the configuration of object parts, yet contemporary vision models primarily harvest local texture cues, yielding brittle, non-compositional features. Work on shape-vs-texture bias has pitted shape and texture representations in opposition, measuring shape relative to texture, ignoring the possibility that models (and humans) can simultaneously rely on both types of cues, and obscuring the absolute quality of both types of representation. We therefore recast shape evaluation as a matter of absolute configural competence, operationalized by the Configural Shape Score (CSS), which (i) measures the ability to recognize both images in Object-Anagram pairs that preserve local texture while permuting global part arrangement to depict different object categories. Across 86 convolutional, transformer, and hybrid models, CSS (ii) uncovers a broad spectrum of configural sensitivity with fully self-supervised and language-aligned transformers -- exemplified by DINOv2, SigLIP2 and EVA-CLIP -- occupying the top end of the CSS spectrum. Mechanistic probes reveal that (iii) high-CSS networks depend on long-range interactions: radius-controlled attention masks abolish performance showing a distinctive U-shaped integration profile, and representational-similarity analyses expose a mid-depth transition from local to global coding. A BagNet control remains at chance (iv), ruling out "border-hacking" strategies. Finally, (v) we show that configural shape score also predicts other shape-dependent evals. Overall, we propose that the path toward truly robust, generalizable, and human-like vision systems may not lie in forcing an artificial choice between shape and texture, but rather in architectural and learning frameworks that seamlessly integrate both local-texture and global configural shape.