3 papers
cs.CV2026
Large Language Models are Universal Reasoners for Visual Generation
Sucheng Ren, Chen Chen, Zhenbang Wang +5
Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM backbone handles both visual un…
cs.CV2025
Autoregressive Video Generation beyond Next Frames Prediction
Sucheng Ren, Chen Chen, Zhenbang Wang +5
Autoregressive models for video generation typically operate frame-by-frame, extending next-token prediction from language to video's temporal dimension. We question that unlike wo…
cs.CV2025
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling
Xinze Wang, Chen Chen, Yinfei Yang +5
Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performanc…