Zhenwei (ParadoxZW)

ParadoxZW

Geek Repo

Location:Hangzhou, China

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MILVLG

Zhenwei's repositories

NodeGo

A Node.js Web Server for Go Game AI, powered by WGo.js, SabakiHQ/gtp and leela-zero.

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CosAttention2d

a 2D cosine attention module inspired by cosFormer: Rethinking Softmax in Attention(https://arxiv.org/abs/2202.08791)

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NERD

NERD: Named Entity Representations for Disambiguation. 2020大学生服务外包大赛国二

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SamaritanHDU

A roll-call system using face recognition technique and WeChat App Platform.

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Awesome-Multimodal-Large-Language-Models

Latest Papers and Datasets on Multimodal Large Language Models

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prophet

Implementation of CVPR 2023 paper "Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering".

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ANNS

project to explore approximate nearest neighborhood search method.

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ChuanhuChatGPT

GUI for ChatGPT API

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cosFormer

Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

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cosformer-pytorch

Unofficial PyTorch implementation of the paper "cosFormer: Rethinking Softmax In Attention".

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fancy-and-tricky

remarkable snippets!

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hexo-deploy-github-pages-action

🚀 GitHub action for deploying a Hexo project to GitHub pages.

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image-processing-from-scratch

This project contains some interesting image processing algorithms that were wrote in python and c++ from scratch.

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imp

Powerful multimodal small language models

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LLaVA

[NeurIPS 2023 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards multimodal GPT-4 level capabilities.

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mmnas

Deep Multimodal Neural Architecture Search

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mySIFT

course project

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openvqa

A lightweight, scalable, and general framework for visual question answering research

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PPOxFamily

PPO x Family DRL Tutorial Course(决策智能入门级公开课:8节课帮你盘清算法理论,理顺代码逻辑,玩转决策AI应用实践 )

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shell_display.py

Display a image in shell using 20 lines Python code.

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Sketch2Attributes

predict the attributes of a sketch of humans

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Test

test some GitHub feature

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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