multimodal-deep-learning
17 proyectos comparten este topic de GitHub
Las líneas conectan a los miembros que están mediblemente relacionados entre sí. El tamaño de los puntos refleja las estrellas.
🧬 Miembros
LAVIS
LAVIS - A One-stop Library for Language-Vision Intelligence
★ 11.3k
FinRobot
FinRobot: An Open-Source AI Agent Platform for Financial Applications using LLMs 🚀 🚀 🚀
★ 7.7k
Time-LLM
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large…
★ 2.7k
Awesome-Text-to-Image
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
★ 2.4k
BitNet
Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch
★ 1.9k
AdvancedLiterateMachinery
A collection of original, innovative ideas and algorithms towards Advanced Literate Machinery. This project…
★ 1.8k
CVPR2024-Papers-with-Code-Demo
收集 CVPR 最新的成果，包括论文、代码和demo视频等，欢迎大家推荐！Collect the latest…
★ 1.4k
awesome-grounding
awesome grounding: A curated list of research papers in visual grounding
★ 1.1k
awesome-multimodal-in-medical-imaging
A collection of resources on applications of multi-modal learning in medical imaging.
★ 973
blended-latent-diffusion
Official implementation for "Blended Latent Diffusion" [SIGGRAPH 2023]
★ 632
MMMU
This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding…
★ 590
OmniShow
[ICML 2026] ByteDance's All-in-One Video Generation Model for Human-Object Interaction Video Generation
★ 462
Med-PaLM
Towards Generalist Biomedical AI
★ 432
Awesome-Parameter-Efficient-Transfer-Learning
A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal…
★ 412
Recommendation-Systems-without-Explicit-ID-Features-A-Literature-Review
Paper List of Pre-trained Foundation Recommender Models
★ 368
Awesome-Multimodal-Papers
A curated list of awesome Multimodal studies.
★ 343
CLoT
CVPR'24, Official Codebase of our Paper: "Let's Think Outside the Box: Exploring Leap-of-Thought in Large…
★ 324
🔗 Familias relacionadas
Medido a partir de los temas de GitHub compartidos por ambos proyectos, ponderado por cuán raros son cada uno de los temas.