# Transductive Visual Verb Sense Disambiguation

arXiv: https://arxiv.org/abs/2012.10821 · HTML: https://mlsanity.com/paper/2012.10821/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation](https://mlsanity.com/paper/2306.14067/) (2023)
- [Bridging Lexical Ambiguity and Vision: A Mini Review on Visual Word Sense Disambiguation](https://mlsanity.com/paper/2602.01193/) (2026)

## Abstract (arXiv)

Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is assigned considering the content of an image paired with it rather than a sentence in which the verb appears. Annotating a dataset for this task is more complex than textual disambiguation, because assigning the correct sense to a pair of <image, verb> requires both non-trivial linguistic and visual skills. In this work, differently from the literature, the VVSD task will be performed in a transductive semi-supervised learning (SSL) setting, in which only a small amount of labeled information is required, reducing tremendously the need for annotated data. The disambiguation process is based on a graph-based label propagation method which takes into account mono or multimodal representations for <image, verb> pairs. Experiments have been carried out on the recently published dataset VerSe, the only available dataset for this task. The achieved results outperform the current state-of-the-art by a large margin while using only a small fraction of labeled samples per sense. Code available: https://github.com/GiBg1aN/TVVSD.