4 papers
SHIFT: Motion Alignment in Video Diffusion Models with Adversarial Hybrid Fine-Tuning
Xi Ye, Wenjia Yang, Yangyang Xu +4
Image-conditioned video diffusion models achieve impressive visual realism but often suffer from weakened motion fidelity, e.g., reduced motion dynamics or degraded long-term tempo…
MaLoRA: Gated Modality LoRA for Key-Space Alignment in Multimodal LLM Fine-Tuning
Xinhan Zheng, Huyu Wu, Xueting Wang +2
Multimodal large language models (MLLMs) exhibit a pronounced preference for textual inputs when processing vision-language data, limiting their ability to reason effectively from…
Diffusion Models as Dataset Distillation Priors
Duo Su, Huyu Wu, Huanran Chen +4
Dataset distillation aims to synthesize compact yet informative datasets from large ones. A significant challenge in this field is achieving a trifecta of diversity, generalization…
Multi-Task Dense Prediction Fine-Tuning with Mixture of Fine-Grained Experts
Yangyang Xu, Xi Ye, Duo Su
Multi-task learning (MTL) for dense prediction has shown promising results but still faces challenges in balancing shared representations with task-specific specialization. In this…