✨✨Latest Advances on Multimodal Large Language Models

Mobile-Agent: The Powerful Mobile Device Operation Assistant Family

ModelScope-Agent: An agent framework connecting models in ModelScope with the world

LLaMA-Omni is a low-latency and high-quality end-to-end speech interaction model built upon Llama-3.1-8B-Instruct, aiming to achieve speech capabilities at the GPT-4o level.

Cambrian-1 is a family of multimodal LLMs with a vision-centric design.

[ICML 2024] Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs (RPG)

mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding

✨✨VITA: Towards Open-Source Interactive Omni Multimodal LLM

A family of lightweight multimodal models.

LLaVA-Plus: Large Language and Vision Assistants that Plug and Learn to Use Skills

✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models. The first work to correct hallucinations in MLLMs.

Speech, Language, Audio, Music Processing with Large Language Model

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

A novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings.

[CVPR 2024] MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

实时语音交互数字人，支持端到端语音方案（GLM-4-Voice - THG）和级联方案（ASR-LLM-TTS-THG）。可自定义形象与音色，无须训练，支持音色克隆，首包延迟低至3s。Real-time voice interactive digital human, supporting end-to-end voice solutions (GLM-4-Voice - THG) and cascaded solutions (ASR-LLM-TTS-THG). Customizable appearance and voice, supporting voice cloning, with initial package delay as low as 3s.

✨✨Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

MLCD & UNICOM : Large-Scale Visual Representation Model

NeurIPS 2024 Paper: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing

The Paper List of Large Multi-Modality Model, Parameter-Efficient Finetuning, Vision-Language Pretraining, Conventional Image-Text Matching for Preliminary Insight.