Pareto LoRA applies Pareto-optimal gradient integration to balance text and image objectives in LoRA-based fine-tuning of unified multimodal models, reporting up to 44.9% gains in image quality on the CoMM benchmark with Emu2 while preserving text performance.
Openleaf: Open-domain interleaved image-text generation and evalua- tion.arXiv preprint arXiv:2310.07749, 2023
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
citing papers explorer
-
Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration
Pareto LoRA applies Pareto-optimal gradient integration to balance text and image objectives in LoRA-based fine-tuning of unified multimodal models, reporting up to 44.9% gains in image quality on the CoMM benchmark with Emu2 while preserving text performance.
-
Toward Native Multimodal Modeling: A Roadmap
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.