most citedLLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

119 citations · 180 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV202325 cited

SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Ziyi Lin, Chris Liu, Renrui Zhang +13

We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-languag…

cs.MM202325 cited

ImageBind-LLM: Multi-modality Instruction Tuning

Jiaming Han, Renrui Zhang, Wenqi Shao +14

We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tu…

cs.CV20236 cited

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

Ziyu Guo, Renrui Zhang, Xiangyang Zhu +8

We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space betwee…

cs.CV2023119 cited

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Peng Gao, Jiaming Han, Renrui Zhang +9

How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains…

cs.CV20235 cited

Few-Shot Object Detection via Variational Feature Aggregation

Jiaming Han, Yuqiang Ren, Jian Ding +2

As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples,the learned models are usually biased to base classes and sensit…