MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts
arXiv:2607.00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation. However, VAR still suffers from inherent deficiencies in multi-scale representation learning. Specifically, lower scales primarily capture global semantics, while higher scales focus on fine-grained details. Employing a shared architecture across scales induces optimization conflicts. Moreover, due to the causal autoregressive process, inaccurate semantics at early scales can
The paper addresses current limitations in multi-scale representation learning for Visual AutoRegressive models, indicating continuous advancements in AI generative capabilities.
Improved VAR models could lead to more accurate and efficient image generation, impacting various applications from synthetic data creation to visual content production.
The proposed MEPA framework aims to resolve optimization conflicts and inaccuracies in multi-scale visual representation, potentially leading to a new standard in VAR model design.
- · AI researchers and developers
- · Generative AI companies
- · Sectors using synthetic visual data
- · Computer vision applications
- · Developers of less efficient VAR models
- · Companies reliant on older generative image techniques
Enhancement of image generation quality and efficiency through multi-scale representation alignment.
Accelerated development of more sophisticated visual AI tools and applications across industries.
Potential for new forms of media creation and simulation environments with hyper-realistic visuals.
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Read at arXiv cs.AI