Unified Multimodal Understanding via Byte-Pair Visual Encoding

2025-06-30

AI Summary

This work addresses the low alignment efficiency and insufficient fusion between visual and linguistic modalities in multimodal large language models (MLLMs). To this end, we propose a vision-oriented Byte-Pair Encoding (BPE) method—the first to directly adapt text tokenization mechanisms to the visual token space. Our approach integrates spatial consistency constraints with frequency-prioritized token merging and employs a curriculum-learning-driven multi-stage training paradigm within the Transformer architecture to enable fine-grained cross-modal modeling. Experiments demonstrate substantial performance gains across multiple vision-language understanding benchmarks, significantly enhancing the model’s capacity to capture cross-modal semantic relationships. The proposed framework establishes a novel paradigm for developing efficient, unified multimodal foundation models.

Technology Category

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

Application Category

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

Abstract

Multimodal large language models (MLLMs) have made significant progress in vision-language understanding, yet effectively aligning different modalities remains a fundamental challenge. We present a framework that unifies multimodal understanding by applying byte-pair encoding to visual tokens. Unlike conventional approaches that rely on modality-specific encoders, our method directly incorporates structural information into visual tokens, mirroring successful tokenization strategies in text-only language models. We introduce a priority-guided encoding scheme that considers both frequency and spatial consistency, coupled with a multi-stage training procedure based on curriculum-driven data composition. These enhancements enable the transformer model to better capture cross-modal relationships and reason with visual information. Comprehensive experiments demonstrate improved performance across diverse vision-language tasks. By bridging the gap between visual and textual representations, our approach contributes to the advancement of more capable and efficient multimodal foundation models.

Problem

• Aligning different modalities in vision-language understanding
• Incorporating structural information into visual tokens
• Improving cross-modal relationships and reasoning

Innovation

• Byte-pair encoding for visual tokens
• Priority-guided frequency and spatial encoding
• Curriculum-driven multi-stage training procedure