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Found 5 papers, 1 papers with code
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT).
NSF: Neural Surface Fields for Human Modeling from Monocular Depth
Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining.
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks.
Event-based Non-Rigid Reconstruction from Contours
Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras.
Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information
Our approach achieves the bestperformance on precision and accuracy of human fall event detection, compared with other existing dictionary learning methods.
