Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs

• URL: http://arxiv.org/abs/2507.00754v1
• Date: Tue, 01 Jul 2025 13:58:21 GMT
• Title: Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs
• Authors: Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva, Federico Tombari, Bernt Schiele,
• Abstract summary: Vision Transformers (ViTs) can be integrated with Large Language Model (LLMs) blocks for vision-only tasks.<n>Direct fusion often fails to fully exploit the LLM's potential and suffers from unstable finetuning.<n>LUViT bridges this modality mismatch through a synergistic pre-training strategy.
• Score: 89.76543013729594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the rich semantic knowledge and reasoning capabilities of LLMs. However, a fundamental challenge lies in the inherent modality mismatch between text-centric pretraining of LLMs and vision-centric training of ViTs. Direct fusion often fails to fully exploit the LLM's potential and suffers from unstable finetuning. As a result, LLM blocks are kept frozen while only the vision components are learned. As a remedy to these challenges, we introduce Language-Unlocked Vision Transformers (LUViT), a novel approach that bridges this modality mismatch through a synergistic pre-training strategy. LUViT co-adapts a ViT backbone and an LLM fusion block by (1) employing Masked Auto-Encoding (MAE) to pre-train the ViT for richer visual representations, and (2) concurrently training Low-Rank Adaptation (LoRA) layers within the LLM block using the MAE objective. This joint optimization guides the ViT to produce LLM-aligned features and the LLM to effectively interpret visual information. We demonstrate through extensive experiments that LUViT significantly improves performance on various downstream vision tasks, showcasing a more effective and efficient pathway to harness LLM knowledge for visual understanding.

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