31 citations · 60 across the 13 of their papers we have counts for
10 papers · 1 filter
ScaleNet: Scaling up Pretrained Neural Networks with Incremental Parameters
Zhiwei Hao, Jianyuan Guo, Li Shen +4
Recent advancements in vision transformers (ViTs) have demonstrated that larger models often achieve superior performance. However, training these models remains computationally in…
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning
Zhiwei Hao, Jianyuan Guo, Li Shen +3
Recent advancements in multimodal fusion have witnessed the remarkable success of vision-language (VL) models, which excel in various multimodal applications such as image captioni…
GhostNetV3: Exploring the Training Strategies for Compact Models
Zhenhua Liu, Zhiwei Hao, Kai Han +2
Compact neural networks are specially designed for applications on edge devices with faster inference speed yet modest performance. However, training strategies of compact models a…
Data-efficient Large Vision Models through Sequential Autoregression
Jianyuan Guo, Zhiwei Hao, Chengcheng Wang +5
Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended…
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution
Chengcheng Wang, Zhiwei Hao, Yehui Tang +4
Diffusion-based super-resolution (SR) models have recently garnered significant attention due to their potent restoration capabilities. But conventional diffusion models perform no…
One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation
Zhiwei Hao, Jianyuan Guo, Kai Han +4
Knowledge distillation~(KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillat…