  Language is ambiguous; many terms and expressions can convey the same idea.
This is especially true in creative practice, where ideas and design intents
are highly subjective. We present a dataset, Ambiguous Descriptions of Art
Images (ADARI), of contemporary workpieces, which aims to provide a
foundational resource for subjective image description and multimodal word
disambiguation in the context of creative practice. The dataset contains a
total of 240k images labeled with 260k descriptive sentences. It is
additionally organized into sub-domains of architecture, art, design, fashion,
furniture, product design and technology. In subjective image description,
labels are not deterministic: for example, the ambiguous label dynamic might
correspond to hundreds of different images. To understand this complexity, we
analyze the ambiguity and relevance of text with respect to images using the
state-of-the-art pre-trained BERT model for sentence classification. We provide
a baseline for multi-label classification tasks and demonstrate the potential
of multimodal approaches for understanding ambiguity in design intentions. We
hope that ADARI dataset and baselines constitute a first step towards
subjective label classification.
