Reading list for research topics in multimodal machine learning

An open-source framework for training large multimodal models.

A curated list of Multimodal Related Research.

ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

A Comparative Framework for Multimodal Recommender Systems

[CVPR'24] UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition

An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"

Papers, code and datasets about deep learning and multi-modal learning for video analysis

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

[CVPR2023 Highlight] GRES: Generalized Referring Expression Segmentation

Multimodal model for text and tabular data with HuggingFace transformers as building block for text data

Multi-Modal learning toolkit based on PaddlePaddle and PyTorch, supporting multiple applications such as multi-modal classification, cross-modal retrieval and image caption.

Official Pytorch implementation of "OmniNet: A unified architecture for multi-modal multi-task learning" | Authors: Subhojeet Pramanik, Priyanka Agrawal, Aman Hussain

[ICCV 2023] MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions

Multi-modality pre-training

[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!

A curated list of awesome vision and language resources (still under construction... stay tuned!)

Research Trends in LLM-guided Multimodal Learning.

A collection of resources on applications of multi-modal learning in medical imaging.