3 citations · 8 across the 6 of their papers we have counts for
6 papers
Dynamic-in-Few-Step: Unifying Dynamic Computation and Few-Step Distillation for Efficient Video Generation
Yu Cheng, Siyue Yao, Zhongang Qi +3
Video Diffusion Models (VDMs) have demonstrated superior generation quality but suffer from prohibitive computational costs. While recent few-step distillation techniques significa…
LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models
Yu Cheng, Fajie Yuan
Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video…
A Content-Driven Micro-Video Recommendation Dataset at Scale
Yongxin Ni, Yu Cheng, Xiangyan Liu +5
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and…
An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
Yu Cheng, Yunzhu Pan, Jiaqi Zhang +3
Recommender systems (RS) have achieved significant success by leveraging explicit identification (ID) features. However, the full potential of content features, especially the pure…
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Jiaqi Zhang, Yu Cheng, Yongxin Ni +6
Large foundational models, through upstream pre-training and downstream fine-tuning, have achieved immense success in the broad AI community due to improved model performance and s…
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Ruyu Li, Wenhao Deng, Yu Cheng +3
Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent item…