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State-of-the-art visual grounding models can achieve high detection accuracy,but they are not designed to distinguish between all objects versus onlycertain objects of interest. In natural language, in order to specify aparticular object or set of objects of interest, humans use determiners such as"my", "either" and "those". Determiners, as an important word class, are a typeof 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 makesit difficult to develop models that understand the full variety and complexityof object referencing. Thus, we have developed and released the DetermiNetdataset , which comprises 250,000 synthetically generated images and captionsbased on 25 determiners. The task is to predict bounding boxes to identifyobjects of interest, constrained by the semantics of the given determiner. Wefind that current state-of-the-art visual grounding models do not perform wellon the dataset, highlighting the limitations of existing models on referenceand quantification tasks.

DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using Determiners