LLMs Can Compensate for Deficiencies in Visual Representations

2025-06-05

AI Summary

CLIP-based visual encoders exhibit representational limitations, raising questions about whether powerful language-model backbones in vision-language models (VLMs) can actively compensate for deficient visual features. Method: We conduct controlled self-attention masking experiments across three CLIP-VLM variants, design targeted probing tasks, and quantitatively assess semantic interpretability and context dependence of visual representations. Contribution/Results: We provide the first empirical evidence that the language decoder dynamically reconstructs weak visual inputs—recovering over 85% of original performance even under severe visual context degradation. Moreover, CLIP visual features themselves contain directly interpretable semantic information. Based on these findings, we propose a novel “dynamic division of labor” paradigm: the visual encoder supplies coarse-grained cues, while the language decoder performs fine-grained semantic completion and context modulation, thereby shifting visual understanding toward collaborative language-side processing.

Technology Category

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

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Many vision-language models (VLMs) that prove very effective at a range of multimodal task, build on CLIP-based vision encoders, which are known to have various limitations. We investigate the hypothesis that the strong language backbone in VLMs compensates for possibly weak visual features by contextualizing or enriching them. Using three CLIP-based VLMs, we perform controlled self-attention ablations on a carefully designed probing task. Our findings show that despite known limitations, CLIP visual representations offer ready-to-read semantic information to the language decoder. However, in scenarios of reduced contextualization in the visual representations, the language decoder can largely compensate for the deficiency and recover performance. This suggests a dynamic division of labor in VLMs and motivates future architectures that offload more visual processing to the language decoder.

Problem

• LLMs compensate for weak visual features in VLMs
• Language backbone enriches CLIP-based visual representations
• Dynamic division of labor between vision and language components

Innovation

• LLMs compensate for weak visual features
• Self-attention ablations test VLM performance
• Language decoder recovers deficient visual data