PL's starred repositories
Semantic-Segment-Anything
Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).
alphafold2
To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
prompt-in-context-learning
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
federated-learning
A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561
Awesome_Prompting_Papers_in_Computer_Vision
A curated list of prompt-based paper in computer vision and vision-language learning.
awesome-multi-modal-reinforcement-learning
A curated list of Multi-Modal Reinforcement Learning resources (continually updated)
multiple-attention
The code of multi-attention deepfake detection
MM-CelebA-HQ-Dataset
[CVPR 2021] A large-scale face image dataset that allows text-to-image generation, text-guided image manipulation, sketch-to-image generation, GANs for face generation and editing, image caption, and VQA
P4Transformer
Implementation of the "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos" paper.
track2_aicity_2021
This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges
Pixel-Level-Cycle-Association
Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
3D-Magic-Mirror
:dress:3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective:dress: Single-View 3D Reconstruction
metapoison
Craft poisoned data using MetaPoison
Dataset-Pruning
Dataset pruning for ImageNet and LAION-2B.
ERGAN-Pytorch
Official code for "Unsupervised Eyeglasses Removal in the Wild",👓🥽🕶
label-distillation
Official PyTorch implementation of “Flexible Dataset Distillation: Learn Labels Instead of Images”
margin-openset
[ICCV2019] Attract or Distract: Exploit the Margin of Open Set