288 citations · 299 across the 16 of their papers we have counts for
6 papers · 2 filters
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks
Yiming Wu, Wei Ji, Kecheng Zheng +2
Recently, human motion analysis has experienced great improvement due to inspiring generative models such as the denoising diffusion model and large language model. While the exist…
Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
Qihe Pan, Zhen Zhao, Zicheng Wang +5
A plethora of text-guided image editing methods has recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models especially St…
Individual Content and Motion Dynamics Preserved Pruning for Video Diffusion Models
Yiming Wu, Zhenghao Chen, Huan Wang +1
The high computational cost and slow inference time are major obstacles to deploying Video Diffusion Models (VDMs). To overcome this, we introduce a new Video Diffusion Model Compr…
PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
Zicheng Wang, Zhenghao Chen, Yiming Wu +3
Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, the…
SOEDiff: Efficient Distillation for Small Object Editing
Yiming Wu, Qihe Pan, Zhen Zhao +3
In this paper, we delve into a new task known as small object editing (SOE), which focuses on text-based image inpainting within a constrained, small-sized area. Despite the remark…
Training-Free Unsupervised Prompt for Vision-Language Models
Sifan Long, Linbin Wang, Zhen Zhao +4
Prompt learning has become the most effective paradigm for adapting large pre-trained vision-language models (VLMs) to downstream tasks. Recently, unsupervised prompt tuning method…