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

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

Diffusion-LM

Release for Improved Denoising Diffusion Probabilistic Models

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

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

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

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

[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning

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

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

The Munich Open-Source Large-Scale Multimedia Feature Extractor

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

[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 Seq…

Fusion Modality Approaches for sentiment analysis and emotion recognition task.

Pytorch Implementation of Tensor Fusion Networks for multimodal sentiment analysis.

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

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

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

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

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

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

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

论文：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)

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