Recent Advances in Vision-Language Pre-training!
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Updated
Jan 10, 2022
Recent Advances in Vision-Language Pre-training!
A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners
[ECCV 2022] Official pytorch implementation of "mc-BEiT: Multi-choice Discretization for Image BERT Pre-training" in European Conference on Computer Vision (ECCV) 2022.
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".
Code of CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
Official Codes for "Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality"
Pytorch reimplementation of "A Unified View of Masked Image Modeling".
PyTorch reimplementation of "A simple, efficient and scalable contrastive masked autoencoder for learning visual representations".
This is a PyTorch implementation of “Context AutoEncoder for Self-Supervised Representation Learning"
[NeurIPS2022] Official implementation of the paper 'Green Hierarchical Vision Transformer for Masked Image Modeling'.
PyTorch code for MUST
ConvMAE: Masked Convolution Meets Masked Autoencoders
code for "AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+"
PyTorch implementation for "Training and Inference on Any-Order Autoregressive Models the Right Way", NeurIPS 2022 Oral, TPM 2023 Best Paper Honorable Mention
Self-Supervised Representation Learning of Semiconductor Wafer Maps using PyTorch
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning
Pytorch implementation of an energy transformer - an energy-based reccurrent variant of the transformer.
[ICCV 2023] You Only Look at One Partial Sequence
Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357
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