发现大模型常把属性错配给同类别物体，提出可量化该错误的新基准。
Diagnosing Dense Same-Class Attribute Misbinding in Large Vision-Language Models
- 构建新基准InstaBind-Lite，精准测量属性错配问题。
- 开源模型平均错配率19.84%，商用接口仅7.55%，但都被整体准确率掩盖。
- 80%以上错配来自相邻物体，定位和实例优先干预效果有限。
大型视觉语言模型虽能识别密集场景中的物体与属性，却常将属性错误分配给同类别对象。通用VQA准确率将此类回答标记为错误，而物体幻觉指标可能误判对象与属性均为图像支持；二者均无法揭示属性转移。本研究将此盲点定义为密集同类别属性错配（DSCAM），提出InstaBind-Lite——一个可控的基准测试，可直接测量该问题。其包含524张图像、529组3-6个同类别实体、1773个框定实例、有序邻近关系、可区分的颜色类属性及四类互补问题，生成9580个可确定评估的问题。与现有协议不同，源实例标注将无依据生成与识别失败，与从可见实体复制属性区分开。绑定特定度量进一步量化转移频率、邻近性、序数距离及干预影响。在五个开源和两个商用/API模型中，开源系统平均错配率为19.84%，商用系统为7.55%；这些错误被整体准确率隐藏。在可识别转移中，分别有80.70%和81.51%源自相邻实例。定位与实例优先干预对部分模型有效，但非普适解法。InstaBind-Lite因此将原本无法区分的错误答案转化为可溯源的故障类别，并检验传统基准无法衡量的可靠性维度：模型不仅知道可见物，更清楚每个属性属于哪个实例。
原文摘要 · Abstract (English)
Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Generic visual-question-answering accuracy marks the response as wrong, while object-hallucination metrics may regard both the object and attribute as image-supported; neither reveals the transfer. This study formalizes this blind spot as Dense Same-Class Attribute Misbinding (DSCAM) and presents InstaBind-Lite, a controlled benchmark that makes it directly measurable. Its 524 images contain 529 curated groups of 3-6 same-class entities, 1773 boxed instances, ordered neighbors, distinguishable color-like attributes, and four complementary question levels, yielding 9580 deterministically evaluated questions. Unlike existing protocols, source-instance annotations separate unsupported generation and recognition failure from an attribute copied from another visible entity. Binding-specific metrics further quantify transfer frequency, adjacency, ordinal distance, and intervention effects. Across five open-source and two commercial/API models, the open-source systems average 19.84% Misbinding Rate and the API systems 7.55%; these errors are hidden by aggregate accuracy. Among identifiable transfers, 80.70% and 81.51%, respectively, originate from adjacent instances. Localization and instance-first interventions help selected models but are not universal remedies. InstaBind-Lite therefore turns previously undifferentiated wrong answers into source-identifiable failure categories and tests a reliability dimension that conventional benchmarks cannot determine: whether a model knows not only what is visible, but which instance owns each attribute.
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