most citedLoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

4 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Motion Graph Unleashed: A Novel Approach to Video Prediction

Yiqi Zhong, Luming Liang, Bohan Tang +2

We introduce motion graph, a novel approach to the video prediction problem, which predicts future video frames from limited past data. The motion graph transforms patches of video…

cs.CV20243 cited

FORA: Fast-Forward Caching in Diffusion Transformer Acceleration

Pratheba Selvaraju, Tianyu Ding, Tianyi Chen +2

Diffusion transformers (DiT) have become the de facto choice for generating high-quality images and videos, largely due to their scalability, which enables the construction of larg…

cs.CV2024

AdaContour: Adaptive Contour Descriptor with Hierarchical Representation

Tianyu Ding, Jinxin Zhou, Tianyi Chen +3

Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single gl…

cs.CV2024

S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing

Guangzhi Wang, Tianyi Chen, Kamran Ghasedi +6

Face attribute editing plays a pivotal role in various applications. However, existing methods encounter challenges in achieving high-quality results while preserving identity, edi…

cs.CV2023

Lightweight Portrait Matting via Regional Attention and Refinement

Yatao Zhong, Ilya Zharkov

We present a lightweight model for high resolution portrait matting. The model does not use any auxiliary inputs such as trimaps or background captures and achieves real time perfo…

cs.CL20234 cited

LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

Tianyi Chen, Tianyu Ding, Badal Yadav +2

Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs. W…