SHI Labs (SHI-Labs)

SHI Labs

SHI-Labs

Geek Repo

Computer Vision, Machine Learning, and AI Systems & Applications

Location:University of Oregon | UIUC

Home Page:https://www.shi-labs.com

Twitter:@humphrey_shi

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SHI Labs's repositories

Versatile-Diffusion

Versatile Diffusion: Text, Images and Variations All in One Diffusion Model, 2022

Language:PythonLicense:MITStargazers:969Issues:24Issues:18

Neighborhood-Attention-Transformer

Official NAT (Neighborhood Attention Transformer) and DiNAT (Dilated Neighborhood Attention Transformer) repository.

Language:PythonLicense:MITStargazers:733Issues:15Issues:53

OneFormer

[Preprint] OneFormer: One Transformer to Rule Universal Image Segmentation, 2022

Language:Jupyter NotebookLicense:MITStargazers:506Issues:12Issues:18

Compact-Transformers

[Preprint] Escaping the Big Data Paradigm with Compact Transformers, 2021

Language:PythonLicense:Apache-2.0Stargazers:374Issues:14Issues:52

Rethinking-Text-Segmentation

[CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

Agriculture-Vision

[CVPR 2020 & 2021 & 2022] Agriculture-Vision Dataset, Prize Challenge and Workshop: A joint effort with many great collaborators to bring Agriculture and Computer Vision/AI communities together to benefit humanity!

Convolutional-MLPs

[Preprint] ConvMLP: Hierarchical Convolutional MLPs for Vision, 2021

Language:PythonLicense:Apache-2.0Stargazers:143Issues:3Issues:4

Semi-Supervised-Transfer-Learning

[CVPR 2021] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

Language:Jupyter NotebookLicense:MITStargazers:92Issues:3Issues:8

Unsupervised-Domain-Adaptation-with-Differential-Treatment

[CVPR 2020] Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

FcF-Inpainting

[WACV 2023] Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand

Language:Jupyter NotebookLicense:NOASSERTIONStargazers:76Issues:9Issues:16

SGL-Retinal-Vessel-Segmentation

[MICCAI 2021] Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels: New SOTA on both DRIVE and CHASE_DB1.

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NATTEN

Neighborhood Attention Extension. Bringing attention to a neighborhood near you!

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StyleNAT

New flexible and efficient image generation framework that sets new SOTA on FFHQ-256 with FID 2.05

Language:PythonLicense:MITStargazers:59Issues:7Issues:0

VMFormer

[Preprint] VMFormer: End-to-End Video Matting with Transformer

Language:PythonLicense:NOASSERTIONStargazers:57Issues:6Issues:12

UltraSR-Arbitrary-Scale-Super-Resolution

[Preprint] UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution, 2021

Pseudo-IoU-for-Anchor-Free-Object-Detection

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

Language:PythonStargazers:26Issues:0Issues:0

SH-GAN

[WACV 2023] Image Completion with Heterogeneously Filtered Spectral Hints

DiSparse-Multitask-Model-Compression

[CVPR 2022] DiSparse: Disentangled Sparsification for Multitask Model Compression

Language:Jupyter NotebookStargazers:10Issues:0Issues:0

Mask-Selection-Networks

[CVPR 2021] Youtube-VIS 2021 3rd place, [CVPR 2020] winner DAVIS 2020. Code for mask selection based methods.

OneFormer-Colab

[Colab Demo Code] OneFormer: One Transformer to Rule Universal Image Segmentation.

Language:PythonLicense:MITStargazers:5Issues:1Issues:0

Boosted-Dynamic-Networks

Boosted Dynamic Neural Networks, AAAI 2023

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LIVE-Layerwise-Image-Vectorization

[CVPR 2022 Oral] Towards Layer-wise Image Vectorization

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SeMask-Segmentation

[Preprint] SeMask: Semantically Masked Transformers for Semantic Segmentation.

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AdaFocusV2

[CVPR 2022] AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

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SinNeRF

"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

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VideoINR-Continuous-Space-Time-Super-Resolution

[CVPR 2022] VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

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