New Pareto LoRA method balances text and image gradients in multimodal models

Researchers have introduced Pareto LoRA, a novel method to address modality imbalance in unified multimodal models (UMMs) during parameter-efficient fine-tuning. This imbalance, particularly prevalent in LoRA-based tuning, causes language gradients to overshadow image generation, leading to degraded visual quality. Pareto LoRA reframes multimodal instruction tuning as a bi-objective optimization problem, integrating text and image gradients using a Pareto-optimal strategy to balance their direction and strength. Experiments on the CoMM benchmark with Emu2 showed Pareto LoRA significantly improved multimodal generation balance, yielding up to a 44.9% increase in perceptual image quality while preserving text performance. AI

IMPACT This method could improve the quality and balance of image generation in multimodal AI systems.

RANK_REASON The cluster contains a research paper detailing a new method for multimodal models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• CoMM benchmark
• LoRA
• Pareto LoRA
• unified multimodal models

• paper
• model release

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