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🔭 Yuhui Yuan is currently a senior researcher at the Visual Computing Group of Microsoft Research Asia. He completed his Ph.D., M.S., and B.S. degrees from the Institute of Computing Technology, CAS, Peking University, and Nanjing University in 2022, 2017, and 2014, respectively. Currently, he is leading efforts on (i) developing generative AI technologies to help ship multiple products to Microsoft Designer and (ii) developing the next-generation graphic design engine for high-quality business content generation (e.g., posters, flyers, infographics, diagram, chart, and slides). His recent representative works include LISA for reasoning segmentation (CVPR’2024), COLE for multi-layered and editable graphic design generation, Glyph-ByT5 for accurate visual text rendering, and SPO for human preference learning of diffusion models.
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🔭 He has rich experience in areas such as general visual semantic/instance/panoptic segmentation and object recognition since joining MSRA in 2017. His representative works on segmentation and object detection include OCRNet (ECCV’2020), OCNet (IJCV’2021), and H-DETR (CVPR’2023).
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🔭 Please send an email to yuyua@microsoft.com or researcher.yuanyuhui@gmail.com if you are interested in an internship position related to business content creation and editing or multimodal reasoning and planning.
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SeniorResearcher@MicrosoftResearch
- Beijing
- https://www.microsoft.com/en-us/research/people/yuyua/
- @RainbowYuhui
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AIGText/Glyph-ByT5
AIGText/Glyph-ByT5 Public[ECCV2024] This is an official inference code of the paper "Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering" and "Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Mu…
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AIGText/GlyphControl-release
AIGText/GlyphControl-release Public[NeurIPS2023] This is the official code of the paper "GlyphControl: Glyph Conditional Control for Visual Text Generation"
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RockeyCoss/Prompt-Segment-Anything
RockeyCoss/Prompt-Segment-Anything PublicThis is an implementation of zero-shot instance segmentation using Segment Anything.
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HDETR/H-Deformable-DETR
HDETR/H-Deformable-DETR Public[CVPR2023] This is an official implementation of paper "DETRs with Hybrid Matching".
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HRNet/HRFormer
HRNet/HRFormer Public[ NeurIPS2021] This is an official implementation of our paper "HRFormer: High-Resolution Transformer for Dense Prediction".
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openseg-group/openseg.pytorch
openseg-group/openseg.pytorch PublicThe official Pytorch implementation of OCNet, OCRNet, and SegFix.
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