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cs.CV2025
EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
Xin He, Longhui Wei, Jianbo Ouyang +3
We propose EMMA, an efficient and unified architecture for multimodal understanding, generation and editing. Specifically, EMMA primarily consists of 1) An efficient autoencoder wi…
cs.CV2025
PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards
Shulei Wang, Longhui Wei, Xin He +4
Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, when extended to multiple subject…
cs.CV2025
Mixpert: Mitigating Multimodal Learning Conflicts with Efficient Mixture-of-Vision-Experts
Xin He, Xumeng Han, Longhui Wei +2
Multimodal large language models (MLLMs) require a nuanced interpretation of complex image information, typically leveraging a vision encoder to perceive various visual scenarios.…