UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation
This work addresses a key architectural problem in multimodal AI for researchers and practitioners, offering an incremental improvement over existing unified models.
The authors tackled the challenge of designing a unified architecture for multimodal understanding and generation by analyzing modality alignment patterns, finding that understanding tasks require increasing alignment while generation tasks need decreasing alignment in deeper layers. They introduced UniFork, a Y-shaped architecture with shared shallow layers and task-specific branches, which outperformed fully shared Transformers and matched or exceeded task-specific models in performance.
Unified image understanding and generation has emerged as a promising paradigm in multimodal artificial intelligence. Despite recent progress, the optimal architectural design for such unified models remains an open challenge. In this work, we start by analyzing the modality alignment behaviors of task-specific expert models for understanding and generation, as well as current unified models. Our analysis reveals a crucial observation: understanding tasks benefit from a progressively increasing modality alignment across network depth, which helps build up semantic information for better comprehension; In contrast, generation tasks follow a different trend: modality alignment increases in the early layers but decreases in the deep layers to recover spatial details. These divergent alignment patterns create a fundamental conflict in fully shared Transformer backbones, where a uniform representational flow often leads to performance compromises across two tasks. Motivated by this finding, we introduce UniFork, a novel Y-shaped architecture that shares the shallow layers for cross-task representation learning, while employing task-specific branches in deeper layers to avoid task interference. This design effectively balances shared learning and task specialization. Through extensive ablation experiments, we demonstrate that Unifork consistently outperforms conventional fully shared Transformer architectures, and achieves performance on par with or better than task-specific models.