  Cross-modal retrieval relies on accurate models to retrieve relevant results
for queries across modalities such as image, text, and video. In this paper, we
build upon previous work by tackling the difficulty of evaluating models both
quantitatively and qualitatively quickly. We present DIME (Dataset, Index,
Model, Embedding), a modality-agnostic tool that handles multimodal datasets,
trained models, and data preprocessors to support straightforward model
comparison with a web browser graphical user interface. DIME inherently
supports building modality-agnostic queryable indexes and extraction of
relevant feature embeddings, and thus effectively doubles as an efficient
cross-modal tool to explore and search through datasets.

Multimedia;Artificial Intelligence