Towards Open-domain Vision and Language Understanding with Wikimedia
David Semedo · Companion Proceedings of the Web Conference 2021 · 2021
Current state-of-the-art task-agnostic visio-linguistic approaches, such as ViLBERT [2], are limited to domains in which texts have a visual materialization (e.g. a person running). This work describes a project towards achieving the next generation of models, that can deal with open-domain media, and learn visio-linguistic representations that reflect data’s context, by jointly reasoning over media, a domain knowledge-graph and temporal context. This ambition will be leveraged by a Wikimedia data framework, comprised by comprehensive and high-quality data, covering a wide range of social, cultural, political and other type of events. Towards this goal, we propose a research setup comprised by an open-domain data framework and a set of novel independent research tasks.