This work aims at modeling how the meaning of gradable adjectives of size (‘big’, ‘small’) can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is ‘big’ or ‘small’ in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as ‘fixed’ attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. Models and visual features used in: - Pezzelle, S., Fernandez, R. (2019). Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts. Proceedings of EMNLP-IJCNLP 2019. - Pezzelle, S., Fernandez, R. (2019). Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size. Proceedings of LANTERN 2019 co-located with EMNLP-IJCNLP 2019.