[CVPR2024 Highlight][VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.

Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

本项目是自动化学报中AUTOPLAN的代码地址，使用大语言模型完成了复杂任务的任务规划以及任务执行

Minicpm和MiniCPM-V的项目和教程。包括推理，量化，边端部署，微调，技术报告、应用六个主题

Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision

DAMO-ConvAI: The official repository which contains the codebase for Alibaba DAMO Conversational AI.

PG-Video-LLaVA: Pixel Grounding in Large Multimodal Video Models

MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips

Please refer to our official repo at https://github.com/IVGSZ/Flash-VStream.

Multimodal Video Understanding Framework (MVU)

[NAACL 2024] Official Implementation of paper "Self-Adaptive Sampling for Efficient Video Question Answering on Image--Text Models"

Awesome papers & datasets specifically focused on long-term videos.

Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03299)

Materials for the Hugging Face Diffusion Models Course

《动手做科研》面向科研初学者，一步一步地展示如何入门人工智能科研

Long Context Transfer from Language to Vision

This is the official code of VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding (ECCV 2024)

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

Code for paper "VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos"

该仓库是MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips

(2024CVPR) MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding

Public release for "Explore until Confident: Efficient Exploration for Embodied Question Answering"

Language Repository for Long Video Understanding

A collection of awesome text-to-image generation studies.

Search, organize, discover anything!

[CSUR] A Survey on Video Diffusion Models

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.