Seeing the Abstract: Translating the Abstract Language for Vision Language Models

2025-05-06

AI Summary

Current vision-language models (VLMs) exhibit limited performance in abstract, language-intensive domains such as fashion, primarily due to insufficient coverage of abstract lexical items in pretraining data—resulting in weak abstract semantic representations. This work is the first to systematically demonstrate the critical role of abstract language in multimodal understanding. We propose Abstract-to-Concrete Translation (ACT), a training-free, model-agnostic method that leverages latent-space analysis of pretrained VLMs to uncover implicit semantic mappings between abstract terms and concrete visual concepts, enabling zero-shot abstract-to-concrete semantic transfer. In image–text retrieval, ACT consistently outperforms fine-tuned baselines under both in-domain and cross-domain settings, demonstrating strong generalization and plug-and-play applicability. Our approach establishes a novel paradigm for modeling abstract language in multimodal systems.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Natural language goes beyond dryly describing visual content. It contains rich abstract concepts to express feeling, creativity and properties that cannot be directly perceived. Yet, current research in Vision Language Models (VLMs) has not shed light on abstract-oriented language. Our research breaks new ground by uncovering its wide presence and under-estimated value, with extensive analysis. Particularly, we focus our investigation on the fashion domain, a highly-representative field with abstract expressions. By analyzing recent large-scale multimodal fashion datasets, we find that abstract terms have a dominant presence, rivaling the concrete ones, providing novel information, and being useful in the retrieval task. However, a critical challenge emerges: current general-purpose or fashion-specific VLMs are pre-trained with databases that lack sufficient abstract words in their text corpora, thus hindering their ability to effectively represent abstract-oriented language. We propose a training-free and model-agnostic method, Abstract-to-Concrete Translator (ACT), to shift abstract representations towards well-represented concrete ones in the VLM latent space, using pre-trained models and existing multimodal databases. On the text-to-image retrieval task, despite being training-free, ACT outperforms the fine-tuned VLMs in both same- and cross-dataset settings, exhibiting its effectiveness with a strong generalization capability. Moreover, the improvement introduced by ACT is consistent with various VLMs, making it a plug-and-play solution.

Problem

• VLMs lack ability to handle abstract language in vision tasks
• Fashion domain datasets show high prevalence of abstract terms
• Current VLMs are trained on text corpora lacking abstract words

Innovation

• Training-free Abstract-to-Concrete Translator (ACT) method
• Utilizes pre-trained models and existing databases
• Improves text-to-image retrieval without fine-tuning