Проекты машинного обучения
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SegGPT: Segmenting Everything In Context

📝We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.https://github.com/baaivision/painter
Instruction Tuning with GPT-4

📝Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed.https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

📝In this paper, we address this challenge, and propose GPTQ, a new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly-efficient.https://github.com/thudm/chatglm-6b
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation📝We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation.https://github.com/winfredy/sadtalker

Segment Everything Everywhere All at Once

📝https://github.com/ux-decoder/segment-everything-everywhere-all-at-once