5 papers · 1 filter
From Holistic to Localized: Local Enhanced Adapters for Efficient Visual Instruction Fine-Tuning
Pengkun Jiao, Bin Zhu, Jingjing Chen +2
Efficient Visual Instruction Fine-Tuning (EVIT) seeks to adapt Multimodal Large Language Models (MLLMs) to downstream tasks with minimal computational overhead. However, as task di…
RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models
Pengkun Jiao, Xinlan Wu, Bin Zhu +3
Large Multi-modal Models (LMMs) have significantly advanced a variety of vision-language tasks. The scalability and availability of high-quality training data play a pivotal role i…
Retrieval Augmented Recipe Generation
Guoshan Liu, Hailong Yin, Bin Zhu +3
Given the potential applications of generating recipes from food images, this area has garnered significant attention from researchers in recent years. Existing works for recipe ge…
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
Haibo Yang, Yang Chen, Yingwei Pan +4
Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, espec…
FoodLMM: A Versatile Food Assistant using Large Multi-modal Model
Yuehao Yin, Huiyan Qi, Bin Zhu +3
Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from sat…