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

2026-02-01

AI Summary

This work addresses the challenge of lexical ambiguity in vision-language tasks, where traditional text-only word sense disambiguation methods fall short in multimodal contexts. The paper presents a systematic review of Visual Word Sense Disambiguation (VWSD) and proposes a multimodal disambiguation framework that integrates visual cues with minimal textual context. The approach unifies feature-based, graph-structured, and contrastive embedding strategies, leveraging state-of-the-art models including CLIP, diffusion generative models, and large language models (LLMs) to enable multilingual VWSD system construction. Through synergistic optimization of prompt engineering, fine-tuning, and multilingual adaptation, the proposed method achieves a 6–8% improvement over zero-shot baselines in Mean Reciprocal Rank, demonstrating the efficacy of multimodal collaboration in enhancing disambiguation performance.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Visual Word Sense Disambiguation
• lexical ambiguity
• vision-language tasks
• multimodal disambiguation
• word sense disambiguation

Innovation

• Visual Word Sense Disambiguation
• multimodal fusion
• CLIP
• large language models
• contrastive learning