• Machine Learning
• Multi Task Learning
• Representation Learning
• Visual Question Answering

Inferring knowledge from various sources and data, such as natural language
and visual data, is challenging. Several tasks were presented to reach this aim,
however, it is not just a matter of solving the task, but it is the assessment of
the models’ ability to ground natural language information in the visual world.
GuessWhat?! is an evaluation framework aiming at assessing the performances
of multi-modal conversational models. It is structured as a game in which two
players are collaborating for reaching a common objective, by the means of
generating and answering questions related to a visual scene.
This work presents a new version of the Imagination Module, which is part
of both player architectures and helps them to improve their understanding of
textual and visual information. The presented version integrates the information
about object attributes in the learned representation, to further improve the
generalization and grounding capabilities of the models.