# Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

arXiv: https://arxiv.org/abs/1908.10285 · HTML: https://mlsanity.com/paper/1908.10285/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?](https://mlsanity.com/paper/2109.12243/) (2021)
- [Visually Grounded Concept Composition](https://mlsanity.com/paper/2109.14115/) (2021)
- [QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning](https://mlsanity.com/paper/2205.03075/) (2022)
- [Fixing confirmation bias in feature attribution methods via semantic match](https://mlsanity.com/paper/2307.00897/) (2023)

## Abstract (arXiv)

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. By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks. Crucially, models fail in developing abstract representations of gradable adjectives that can be used compositionally.