Reading list for research topics in multimodal machine learning
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Updated
Jun 19, 2024
Reading list for research topics in multimodal machine learning
An open-source framework for training large multimodal models.
Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)
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!
[CVPR'24] UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
A Comparative Framework for Multimodal Recommender Systems
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
[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning
Multi-modality pre-training
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 collection of resources on applications of multi-modal learning in medical imaging.
A curated list of awesome vision and language resources (still under construction... stay tuned!)
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