对比婴儿与网络训练模型对物体属性的识别能力，发现各有优劣。
Benchmarking Attribute Discrimination in Infant-Scale Vision-Language Models
- 用合成图像控制颜色、大小、纹理，构建可区分属性的测试集
- 婴儿模型在大小和纹理上表现好，但颜色视觉识别差，文本引导下更弱
- 网络模型能很好理解颜色文字，但对大小的视觉辨别力较弱
婴儿从有限经验中学习物体类别及颜色、大小、纹理等细粒度视觉属性。此前的婴儿规模视觉-语言模型主要评估对象识别能力，未考察类内属性区分能力。本文提出一个受控基准，通过合成渲染在67种日常物体类别中独立调节颜色、大小和纹理，以分离属性值与物体身份。在图像仅原型测试和带属性-物体提示的图文测试两种设置下，评估婴儿训练模型（CVCL和婴儿训练DINO基线）与网络规模模型（CLIP、SigLIP、ResNeXt）。结果发现视觉与语言属性信息存在解耦：婴儿模型对大小形成强视觉表征，纹理辨别能力与其它模型相当，但视觉颜色识别表现差；在图文任务中难以锚定颜色，仅表现出适度的大小锚定。相比之下，网络训练模型能从文本中强地锚定颜色，但在视觉大小辨别上表现较弱。
原文摘要 · Abstract (English)
Infants learn not only object categories but also fine-grained visual attributes such as color, size, and texture from limited experience. Prior infant-scale vision--language models have mainly been evaluated on object recognition, leaving open whether they support within-class attribute discrimination. We introduce a controlled benchmark that varies color, size, and texture across 67 everyday object classes using synthetic rendering to decouple attribute values from object identity. We evaluate infant-trained models (CVCL and an infant-trained DINO baseline) against web-scale and ImageNet models (CLIP, SigLIP, ResNeXt) under two complementary settings: an image-only prototype test and a text--vision test with attribute--object prompts. We find a dissociation between visual and linguistic attribute information: infant-trained models form strong visual representations for size and discriminate texture comparably to other models, but perform poorly on visual color discrimination, and in the text--vision setting they struggle to ground color and show only modest size grounding. In contrast, web-trained vision--language models strongly ground color from text while exhibiting weaker visual size discrimination.
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