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https://github.com/jiny419/awesome-mlvid
moment localization in videos
https://github.com/jiny419/awesome-mlvid
List: awesome-mlvid
Last synced: about 1 month ago
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moment localization in videos
- Host: GitHub
- URL: https://github.com/jiny419/awesome-mlvid
- Owner: jiny419
- Created: 2022-04-04T06:09:16.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-02T04:03:12.000Z (6 months ago)
- Last Synced: 2024-05-22T01:00:32.465Z (4 months ago)
- Size: 16.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-mlvid - Moment localization in videos . (Other Lists / PowerShell Lists)
README
# Awesome-MLVU [](https://awesome.re)
🔥 Machine Learning on Video Understanding (MLVU) have taken the **the Whole World** by storm. Here is a curated list of papers about mlvu models. It also contains frameworks for training, courses and tutorials about MLVU and all publicly available checkpoints and APIs.
## Table of Content
- [Awesome-MLVU](#awesome-llm-)
- [Milestone Papers](#milestone-papers)
- [Other Papers](#other-papers)
- [Open MLVU](#open-llm)
- [MLVU Frameworks](#llm-training-frameworks)
- [MLVU Evaluation Frameworks](#llm-evaluation-frameworks)
- [Tutorials about MLVU](#tutorials)
- [Courses about MLVU](#courses)
- [Opinions about MLVU](#opinions)
- [Other Useful Resources](#other-useful-resources)
- [Contributing](#contributing)
## Milestone Papers
| Date | keywords | Institute | Paper | Publication |
| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: |
| 2017-06 | Transformers | Google | [Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf) | NeurIPS
 |
| 2018-06 | GPT 1.0 | OpenAI | [Improving Language Understanding by Generative Pre-Training](https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf) |  |
| 2018-10 | BERT | Google | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423.pdf) | NAACL
 |
| 2019-02 | GPT 2.0 | OpenAI | [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) |  |
| 2019-09 | Megatron-LM | NVIDIA | [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) |  |
| 2019-10 | T5 | Google | [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://jmlr.org/papers/v21/20-074.html) | JMLR
 |
| 2019-10 | ZeRO | Microsoft | [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/pdf/1910.02054.pdf) | SC
 |
| 2020-01 | Scaling Law | OpenAI | [Scaling Laws for Neural Language Models](https://arxiv.org/pdf/2001.08361.pdf) ||
| 2020-05 | GPT 3.0 | OpenAI | [Language models are few-shot learners](https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf) | NeurIPS
 |
| 2021-01 | Switch Transformers | Google | [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/pdf/2101.03961.pdf) | JMLR
 |
| 2021-08 | Codex | OpenAI | [Evaluating Large Language Models Trained on Code](https://arxiv.org/pdf/2107.03374.pdf) ||
## Other Papers
If you're interested in the field of LLM, you may find the above list of milestone papers helpful to explore its history and state-of-the-art. However, each direction of LLM offers a unique set of insights and contributions, which are essential to understanding the field as a whole. For a detailed list of papers in various subfields, please refer to the following link:
- [Awesome-LLM-hallucination](https://github.com/LuckyyySTA/Awesome-LLM-hallucination) - LLM hallucination paper list.
## Open LLM
There are three important steps for a ChatGPT-like LLM:
- **Pre-training**
- **Instruction Tuning**
- **Alignment**
> You may also find these leaderboards helpful:
> - [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) - aims to track, rank and evaluate LLMs and chatbots as they are released.
> - [Chatbot Arena Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) - a benchmark platform for large language models (LLMs) that features anonymous, randomized battles in a crowdsourced manner.
> - [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/) - An Automatic Evaluator for Instruction-following Language Models
> - [Open Ko-LLM Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard) - The Open Ko-LLM Leaderboard objectively evaluates the performance of Korean Large Language Model (LLM).
> - [Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) - Leaderboard made with LLM AutoEval using Nous benchmark suite.
> - [OpenCompass 2.0 LLM Leaderboard](https://rank.opencompass.org.cn/leaderboard-llm-v2) - OpenCompass is an LLM evaluation platform, supporting a wide range of models (InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.
- [Gemma](https://blog.google/technology/developers/gemma-open-models/) - Gemma is built for responsible AI development from the same research and technology used to create Gemini models.
- [Mistral](https://mistral.ai/) - Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases including code and 8k sequence length. Apache 2.0 licence.
- [Mixtral 8x7B](https://mistral.ai/news/mixtral-of-experts/) - a high-quality sparse mixture of experts model (SMoE) with open weights.
## MLVU Training Frameworks
- [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
- [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed) - DeepSpeed version of NVIDIA's Megatron-LM that adds additional support for several features such as MoE model training, Curriculum Learning, 3D Parallelism, and others.
## Tutorials
- [Maarten Grootendorst] A Visual Guide to Mamba and State Space Models [blog](https://maartengrootendorst.substack.com/p/a-visual-guide-to-mamba-and-state?utm_source=multiple-personal-recommendations-email&utm_medium=email&open=false)
- [Jack Cook] [Mamba: The Easy Way](https://jackcook.com/2024/02/23/mamba.html)
## Courses
- [UWaterloo] CS 886: Recent Advances on Foundation Models [Homepage](https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/)
- [DeepLearning.AI] ChatGPT Prompt Engineering for Developers [Homepage](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)
## Books
- [Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs](https://amzn.to/3GUlRng) - it comes with a [GitHub repository](https://github.com/benman1/generative_ai_with_langchain) that showcases a lot of the functionality
- [Build a Large Language Model (From Scratch)](https://www.manning.com/books/build-a-large-language-model-from-scratch) - A guide to building your own working LLM.
## Opinions
- [A Stage Review of Instruction Tuning](https://yaofu.notion.site/June-2023-A-Stage-Review-of-Instruction-Tuning-f59dbfc36e2d4e12a33443bd6b2012c2) [2023-06-29] [Yao Fu]
- [Large Language Models: A New Moore's Law ](https://huggingface.co/blog/large-language-models) \[2021-10-26\]\[Huggingface\]
## Other Useful Resources
- [Arize-Phoenix](https://phoenix.arize.com/) - Open-source tool for ML observability that runs in your notebook environment. Monitor and fine tune LLM, CV and Tabular Models.
- [Emergent Mind](https://www.emergentmind.com) - The latest AI news, curated & explained by GPT-4.
## Contributing
This is an active repository and your contributions are always welcome!
I will keep some pull requests open if I'm not sure if they are awesome for LLM, you could vote for them by adding 👍 to them.
---
If you have any question about this opinionated list, do not hesitate to contact me [email protected].
[^1]: This is not legal advice. Please contact the original authors of the models for more information.