Yezhiqiu's repositories

MiniGPT-4

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models

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GeoKG

geographic knowledge graph

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graph-databases-use-cases

Example use cases from the O'Reilly Graph Databases book

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GraphLite

A lightweight graph computation platform in C/C++

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kg-beijing

北京知识图谱学习小组

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knowledge

Go社区的知识图谱,Knowledge Graph

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LAVIS

LAVIS - A One-stop Library for Language-Vision Intelligence

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LLaVA

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

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neo4j-nlp

NLP Capabilities in Neo4j

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NTU_ML2017_Hung-yi-Lee_HW

NTU ML2017 Spring and Fall Homework Hung-yi_Li 李宏毅老师 机器学习课程作业

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open_clip

An open source implementation of CLIP.

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Osprey

The code for "Osprey: Pixel Understanding with Visual Instruction Tuning"

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realworldnlp

Example code for "Real-World Natural Language Processing"

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ReLA

[CVPR2023 Highlight] GRES: Generalized Referring Expression Segmentation

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rich-text-to-image

Rich-Text-to-Image Generation

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Segment-Everything-Everywhere-All-At-Once

[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"

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Sem-K-BERT

Sem-K-BERT, which can enhance BERT by using knowledge graph and semantic role tag information , and we used location coding to ensure that the normal work of external information would not be interfered with each other. Experiments show that our model has achieved better performance on a wide range of Chinese nlp tasks. This shows that the simultaneous use of knowledge graph and semantic role labeling information can bring greater improvement to BERT.

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SoM

Set-of-Mark Prompting for LMMs

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unilm

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

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