3 papers
cs.LG2025
In-Context Learning can Perform Continual Learning Like Humans
Liuwang Kang, Fan Wang, Shaoshan Liu +3
Large language models (LLMs) can adapt to new tasks via in-context learning (ICL) without parameter updates, making them powerful learning engines for fast adaptation. While extens…
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.CV2023
Sparse Depth-Guided Attention for Accurate Depth Completion: A Stereo-Assisted Monitored Distillation Approach
Jia-Wei Guo, Hung-Chyun Chou, Sen-Hua Zhu +3
This paper proposes a novel method for depth completion, which leverages multi-view improved monitored distillation to generate more precise depth maps. Our approach builds upon th…