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annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

ask-anything

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

awesome-multimodal-llm

Research Trends in LLM-guided Multimodal Learning.

bolei_awesome_posters

CVPR and NeurIPS poster examples and templates. May we have in-person poster session soon!

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

cleva

[EMNLP 2023 Demo] CLEVA: Chinese Language Models EVAluation Platform

conference-acceptance-rate

Acceptance rates for the major AI conferences

cvpr2020_poster

Speech2Action CVPR Poster Source Code

glados-auto-checkin

GLADOS自动签到，多账号签到

glados_auto_checkin

hawkeye

helm

Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of language models (https://arxiv.org/abs/2211.09110).

latex_paper_writing_tips

Tips for Writing a Research Paper using LaTeX

llama-vid

Official Implementation for LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models

llava

[NeurIPS'23 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards GPT-4V level capabilities.

myarxiv

sglang

SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with models faster and more controllable.

st-llm

Official implementation of the paper "ST-LLM: Large Language Models Are Effective Temporal Learners"

tempcompass

A benchmark to evaluate the temporal perception ability of Video LLMs

video-chatgpt

"Video-ChatGPT" is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.

visual-table

Stay tuned!

vl-pet

[ICCV2023] Official code for "VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control"