1 citations · 1 across the 9 of their papers we have counts for
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Autoregressive Visual Generation Needs a Prologue
Bowen Zheng, Weijian Luo, Guang Yang +2
In this work, we propose Prologue, an approach to bridging the reconstruction-generation gap in autoregressive (AR) image generation. Instead of modifying visual tokens to satisfy…
Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
Bowen Zheng, Weijian Luo, Guang Yang +2
Most discrete visual tokenizers rely on a default design: every position in the sequence shares the same codebook. Researchers try to scale the codebook size to get better reco…
Multimodal OCR: Parse Anything from Documents
Handong Zheng, Yumeng Li, Kaile Zhang +22
We present Multimodal OCR (MOCR), a document parsing paradigm that jointly parses text and graphics into unified textual representations. Unlike conventional OCR systems that focus…
dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
Yumeng Li, Guang Yang, Hao Liu +2
Document Layout Parsing serves as a critical gateway for Artificial Intelligence (AI) to access and interpret the world's vast stores of structured knowledge. This process,which en…
David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training
Weijian Luo, Colin Zhang, Debing Zhang +1
We propose Diff-Instruct* (DI*), a data-efficient post-training approach for one-step text-to-image generative models to improve its human preferences without requiring image data.…