# Bridging Lexical Ambiguity and Vision: A Mini Review on Visual Word Sense Disambiguation

arXiv: https://arxiv.org/abs/2602.01193 · HTML: https://mlsanity.com/paper/2602.01193/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Supervised Multimodal Bitransformers for Classifying Images and Text](https://mlsanity.com/paper/1909.02950/) (2019)
- [Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings](https://mlsanity.com/paper/1603.09188/) (2016)
- [Cross-lingual Visual Verb Sense Disambiguation](https://mlsanity.com/paper/1904.05092/) (2019)
- [A Taxonomy of Ambiguity Types for NLP](https://mlsanity.com/paper/2403.14072/) (2024)
- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [Transductive Visual Verb Sense Disambiguation](https://mlsanity.com/paper/2012.10821/) (2020)
- [ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers](https://mlsanity.com/paper/2408.06040/) (2024)
- [Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion](https://mlsanity.com/paper/2307.05564/) (2023)

## Abstract (arXiv)

This paper offers a mini review of Visual Word Sense Disambiguation (VWSD), which is a multimodal extension of traditional Word Sense Disambiguation (WSD). VWSD helps tackle lexical ambiguity in vision-language tasks. While conventional WSD depends only on text and lexical resources, VWSD uses visual cues to find the right meaning of ambiguous words with minimal text input. The review looks at developments from early multimodal fusion methods to new frameworks that use contrastive models like CLIP, diffusion-based text-to-image generation, and large language model (LLM) support. Studies from 2016 to 2025 are examined to show the growth of VWSD through feature-based, graph-based, and contrastive embedding techniques. It focuses on prompt engineering, fine-tuning, and adapting to multiple languages. Quantitative results show that CLIP-based fine-tuned models and LLM-enhanced VWSD systems consistently perform better than zero-shot baselines, achieving gains of up to 6-8% in Mean Reciprocal Rank (MRR). However, challenges still exist, such as limitations in context, model bias toward common meanings, a lack of multilingual datasets, and the need for better evaluation frameworks. The analysis highlights the growing overlap of CLIP alignment, diffusion generation, and LLM reasoning as the future path for strong, context-aware, and multilingual disambiguation systems.