  With the rise and development of deep learning over the past decade, there
has been a steady momentum of innovation and breakthroughs that convincingly
push the state-of-the-art of cross-modal analytics between vision and language
in multimedia field. Nevertheless, there has not been an open-source codebase
in support of training and deploying numerous neural network models for
cross-modal analytics in a unified and modular fashion. In this work, we
propose X-modaler -- a versatile and high-performance codebase that
encapsulates the state-of-the-art cross-modal analytics into several
general-purpose stages (e.g., pre-processing, encoder, cross-modal interaction,
decoder, and decode strategy). Each stage is empowered with the functionality
that covers a series of modules widely adopted in state-of-the-arts and allows
seamless switching in between. This way naturally enables a flexible
implementation of state-of-the-art algorithms for image captioning, video
captioning, and vision-language pre-training, aiming to facilitate the rapid
development of research community. Meanwhile, since the effective modular
designs in several stages (e.g., cross-modal interaction) are shared across
different vision-language tasks, X-modaler can be simply extended to power
startup prototypes for other tasks in cross-modal analytics, including visual
question answering, visual commonsense reasoning, and cross-modal retrieval.
X-modaler is an Apache-licensed codebase, and its source codes, sample projects
and pre-trained models are available on-line:
https://github.com/YehLi/xmodaler.
