3 accepted papers
One-shot pruning efficiently compresses Large Language Models but produces coarse sparse weights, causing significant performance degradation. Traditional fine-tuning approaches to refine these weights are prohibitively expensive for large models. This highlights the need for a training-free weight
We propose FaceCom a method for 3D facial shape completion which delivers high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike end-to-end shape completion methods based on point clouds or voxels our approach relies on a mesh-based generative network that is easy to optimize…
Look-Up Table (LUT) has recently gained increasing attention for restoring High-Quality (HQ) images from Low-Quality (LQ) observations thanks to its high computational efficiency achieved through a "space for time" strategy of caching learned LQ-HQ pairs. However incorporating multiple LUTs for impr…