7 citations · 12 across the 4 of their papers we have counts for
4 papers
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers
Xianbiao Qi, Jianan Wang, Yihao Chen +2
We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previ…
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training
Yihao Chen, Xianbiao Qi, Jianan Wang +1
We propose DisCo-CLIP, a distributed memory-efficient CLIP training approach, to reduce the memory consumption of contrastive loss when training contrastive learning models. Our ap…
Exploring Vision Transformers as Diffusion Learners
He Cao, Jianan Wang, Tianhe Ren +4
Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone whic…
3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume
Jianye Pang, Cheng Jiang, Yihao Chen +4
Dense prediction in medical volume provides enriched guidance for clinical analysis. CNN backbones have met bottleneck due to lack of long-range dependencies and global context mod…