Unified Multimodal Understanding via Byte-Pair Visual Encoding

• URL: http://arxiv.org/abs/2506.23639v1
• Date: Mon, 30 Jun 2025 09:08:08 GMT
• Title: Unified Multimodal Understanding via Byte-Pair Visual Encoding
• Authors: Wanpeng Zhang, Yicheng Feng, Hao Luo, Yijiang Li, Zihao Yue, Sipeng Zheng, Zongqing Lu,
• Abstract summary: Multimodal large language models (MLLMs) have made significant progress in vision-language understanding.<n>We present a framework that unifies multimodal understanding by applying byte-pair encoding to visual tokens.
• Score: 34.96534298857146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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