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About this paper
Shanshan Zhao, Xinjie Zhang, Jintao Guo, Jiakui Hu, Lunhao Duan, Minghao Fu, Yong Xien Chng, Guo-Hua Wang and 4 more
The survey maps design choices for models that understand images and generate them from instructions. It organizes systems by generation backbone and image representation, covering autoregressive, diffusion, and combined approaches, as well as pixel, semantic, query-based, and hybrid encodings. It also gathers datasets and evaluation resources. The survey identifies open questions but establishes no winning architecture or performance.
Key insights
- Unified multimodal models are broadly categorized into three architectural paradigms: Diffusion models (leveraging denoising), Autoregressive models (using LLM backbones), and Fused approaches (combining AR for text and diffusion for images).
- Visual encoding strategies, such as pixel-based, semantic, learnable query, and hybrid methods, are critical for the performance and efficiency of autoregressive multimodal models.
- Key challenges include managing high-dimensionality multimodal data, optimizing cross-modal attention mechanisms, addressing dataset noise and bias, and developing adequate evaluation protocols for integrated tasks.
Summary by alphaXiv.
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