2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks
Ni Ding, Shuchang Wang, Lei He +2
End-to-end neural networks have become a dominant paradigm in autonomous driving, where reliable deployment requires controllable post-training adaptation and improved transparency…
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping
Ning Ding, Jing Han, Yuchuan Tian +3
Diffusion Transformer (DiT) has now become the preferred choice for building image generation models due to its great generation capability. Unlike previous convolution-based UNet…
Enhancing Neural Radiance Fields with Depth and Normal Completion Priors from Sparse Views
Jiawei Guo, HungChyun Chou, Ning Ding
Neural Radiance Fields (NeRF) are an advanced technology that creates highly realistic images by learning about scenes through a neural network model. However, NeRF often encounter…
Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
Shibo Jie, Yehui Tang, Ning Ding +3
Current solutions for efficiently constructing large vision-language (VL) models follow a two-step paradigm: projecting the output of pre-trained vision encoders to the input space…
GPT4Image: Large Pre-trained Models Help Vision Models Learn Better on Perception Task
Ning Ding, Yehui Tang, Zhongqian Fu +3
The upsurge in pre-trained large models started by ChatGPT has swept across the entire deep learning community. Such powerful models demonstrate advanced generative ability and mul…