2 citations · 4 across the 8 of their papers we have counts for
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cs.CL2024★ 1 cited
MemoryFormer: Minimize Transformer Computation by Removing Fully-Connected Layers
Ning Ding, Yehui Tang, Haochen Qin +6
In order to reduce the computational complexity of large language models, great efforts have been made to to improve the efficiency of transformer models such as linear attention a…
cs.CV2024
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…
cs.CV2024★ 1 cited
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…