YuanqingLee

YuanqingLee's Stars

• Justin1904/Low-rank-Multimodal-Fusion

  This is the repository for "Efficient Low-rank Multimodal Fusion with Modality-Specific Factors", Liu and Shen, et. al. ACL 2018

  Language:Python25347

• Shark-NLP/DiffuSeq

  [ICLR'23] DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models

  Language:Python73188

• XiangLi1999/Diffusion-LM

  Diffusion-LM

  Language:Python1.1k136

• openai/guided-diffusion

  Language:Python6.3k822

• openai/improved-diffusion

  Release for Improved Denoising Diffusion Probabilistic Models

  Language:Python3.3k487

• rodrigobressan/face2data

  Face2Data: Extract meaningful information from a person face in less than a second. Powered by Keras and Flask.

  Language:Python11311

• GOATmessi8/RFBNet

  Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

  Language:Python1.4k355

• lzx1413/PytorchSSD

  pytorch version of SSD and it's enhanced methods such as RFBSSD,FSSD and RefineDet

  Language:Python709238

• stanfordnlp/GloVe

  Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings

  Language:C6.9k1.5k

• pliang279/MultiBench

  [NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning

  Language:HTML49070

• CMU-MultiComp-Lab/CMU-MultimodalSDK

  Language:Python17821

• TadasBaltrusaitis/OpenFace

  OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.

  Language:MATLAB6.9k1.9k

• ZhangLeUestc/PersEmoN

  PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

  Language:C++114

• audeering/opensmile

  The Munich Open-Source Large-Scale Multimedia Feature Extractor

  Language:C++59376

• declare-lab/MELD

  MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation

  Language:Python819204

• declare-lab/MM-Align

  [EMNLP 2022] This repository contains the official implementation of the paper "MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences"

  Language:Python241

• gkoumasd/MSAF

  Fusion Modality Approaches for sentiment analysis and emotion recognition task.

  Language:Python122

• Justin1904/TensorFusionNetworks

  Pytorch Implementation of Tensor Fusion Networks for multimodal sentiment analysis.

  Language:Python17644

• YuanqingLee/conv-emotion

  This repo contains implementation of different architectures for emotion recognition in conversations

  1

• project-baize/baize-chatbot

  Let ChatGPT teach your own chatbot in hours with a single GPU!

  Language:Python3.2k285

• arthurdehgan/HOPLS

  Language:Python74

• Timothyxxx/Chain-of-ThoughtsPapers

  A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".

  2k130

• JavierAntoran/Bayesian-Neural-Networks

  Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

  Language:Jupyter Notebook1.8k302

• YuyangXueEd/WUDWNiBDLfCV-pytorch-implementation

  My implementation on What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision

  Language:Python31

• hmi88/what

  Pytorch implementation of "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?"

  Language:Python15720

• pmorerio/dl-uncertainty

  "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017 (unofficial code).

  Language:Python20736

• jiawyGithub/What-Uncertainties

  论文：What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision

  Language:Python21

• ShellingFord221/My-implementation-of-What-Uncertainties-Do-We-Need-in-Bayesian-Deep-Learning-for-Computer-Vision

  Pytorch implementation of classification task in What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision (simple version)

  Language:Python768

• ustc-slr/ChaLearn-2021-ISLR-Challenge

  Code, data and models for our submission to the ChaLearn 2021 LAP challenge (CVPR2021)

  Language:Shell172

• ZhipengLiu6/Chalearn2017_isolated_gesture

  Language:C++147