DetermiNet：一个用于基于限定词的复杂视觉定位引用的大规模诊断数据集
DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using Determiners
AI总结：
本文提出DetermiNet数据集，包含25万张基于25个限定词生成的图像和标题，用于诊断视觉定位模型在复杂对象引用和量化任务上的局限性。
摘要：
最先进的视觉定位模型能够实现高检测准确率，但它们并非被设计用于区分所有对象与仅某些感兴趣的对象。在自然语言中，为了指定特定的对象或感兴趣的对象集合，人类使用诸如“my”、“either”和“those”等限定词。限定词作为一个重要的词类，是自然语言中关于名词引用或数量的一种图式。与名词、动词和形容词等其他词类相比，现有的定位引用数据集对限定词的重视程度要低得多。这使得开发能够理解对象引用的全部多样性和复杂性的模型变得困难。因此，我们开发并发布了DetermiNet数据集，该数据集包含基于25个限定词生成的250,000张合成图像和标题。任务是预测边界框以识别感兴趣的对象，并受给定限定词语义的约束。我们发现当前最先进的视觉定位模型在该数据集上表现不佳，突显了现有模型在引用和量化任务上的局限性。
英文摘要：
State-of-the-art visual grounding models can achieve high detection accuracy, but they are not designed to distinguish between all objects versus only certain objects of interest. In natural language, in order to specify a particular object or set of objects of interest, humans use determiners such as "my", "either" and "those". Determiners, as an important word class, are a type of schema in natural language about the reference or quantity of the noun. Existing grounded referencing datasets place much less emphasis on determiners, compared to other word classes such as nouns, verbs and adjectives. This makes it difficult to develop models that understand the full variety and complexity of object referencing. Thus, we have developed and released the DetermiNet dataset , which comprises 250,000 synthetically generated images and captions based on 25 determiners. The task is to predict bounding boxes to identify objects of interest, constrained by the semantics of the given determiner. We find that current state-of-the-art visual grounding models do not perform well on the dataset, highlighting the limitations of existing models on reference and quantification tasks.
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。
