Search Results for author:
Found 5 papers, 2 papers with code
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D.
Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
Our goal is to develop fine-grained real-image editing methods suitable for real-world applications.
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.
Reference-Based Video Colorization with Spatiotemporal Correspondence
To address this issue, we warp colors only from the regions on the reference frame restricted by correspondence in time.
Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling
We propose a novel out-of-core algorithm that enables faster training of extremely large-scale neural networks with sizes larger than allotted GPU memory.
