Collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

Overview

Reading list in Transformer

We are a team from KAUST Vision-CAIR group and focus on the Multi-modal representation learning.

This repo is aimed to collect all the recent popular Transformer paper, codes and learning resources with respect to the domains of Vision Transformer, NLP and multi-modal, etc.

Recent News

CVPR multi-modal papers are collected in here

The code of VisualGPT is open sourced. They can be found here

The code and paper of LeViT is open sourced. They can be found here

The paper MLP-Mixer: An all-MLP Architecture for Vision is availble here

The code and paper of MDTER is open sourced. They can be found here

The code and papper of RelTransformer is open sourced. They can be found here

The code and paper of Twins-SVT is open sourced. They can be found here

Vision Transformer for deepfake detection. They can be found here

The code of VideoGPT is open sourced. They can be found here

The code of CoaT is open sourced. They can be found here

The code of Kaleido-BERT is open sourced. They can be found here

The code of TimeSformer is open sourced. They can be found here

The code of SwinTransformer is open sourced. They can be found here

Topics (paper and code)

Review Paper in multi-modal

Tutorials and workshop

Datasets

Blogs

Tools

Owner
Jun Chen
I am a PhD candidate student in KAUST. Interested research topic include multi-modal learning and Transformer
Jun Chen
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Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

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Code release for DS-NeRF (Depth-supervised Neural Radiance Fields)

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Implementing DropPath/StochasticDepth in PyTorch

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[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)

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GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

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[CVPR2021] Look before you leap: learning landmark features for one-stage visual grounding.

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Official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning (ICML 2021) published at International Conference on Machine Learning

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Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

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