Popular GitHub Repositories for Selective Masking

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

Context aware, pluggable and customizable data protection and de-identification SDK for text and images

Auto detecting, masking and inpainting with detection model.

PostgreSQL database anonymization and synthetic data generation tool

[CVPR 2023] VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

Code for our CVPR 2019 paper: Selective Kernel Networks; See zhihu：https://zhuanlan.zhihu.com/p/59690223

Tools for Speech Enhancement integrated with Kaldi

The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction

Extension for Automatic1111 and ComfyUI to automatically create masks for Background/Hair/Body/Face/Clothes in Img2Img

Official implementation of OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

Auto masking and inpainting for person, face, hand. Resizing image using detection model.

LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation

Notes on the Mamba and the S4 model (Mamba: Linear-Time Sequence Modeling with Selective State Spaces)

Offical Code of MICCAI'24 early accepted paper "LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos"

An image viewer and AI-assisted editing/captioning/masking tool that helps with curating datasets for generative AI models, finetunes and LoRA.

Implementation of MambaByte in "MambaByte: Token-free Selective State Space Model" in Pytorch and Zeta

[ICML 2023] Official PyTorch Implementation of "Hierarchical Neural Coding for Controllable CAD Model Generation".

Annotate-lab is an open-source image annotation tool for efficient dataset creation. With an intuitive interface and flexible export options, it streamlines your machine learning workflow. ?️✏️?

[ICLR 2023] Code for our paper "Selective Annotation Makes Language Models Better Few-Shot Learners"

Implementation of MoE Mamba from the paper: "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" in Pytorch and Zeta