Name: Bo Zhang
Type: User
Company: Shanghai AI Laboratory
Bio: Bo Zhang received the Ph.D. degree in electronic engineering from Fudan University, in 2022. He is currently a Researcher with Shanghai AI Laboratory.
Location: Shanghai ,China
Blog: https://bobrown.github.io/boZhang.github.io/
Bo Zhang's Projects
An open-source codebase for exploring the Autonomous Driving-oriented Transfer Learning Techniques
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Caffe on both Linux and Windows
A Windows version implementation of SSD
Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation
🔮 ChatGPT Desktop Application (Mac, Windows and Linux)
A tensorflow implementation about conditional generative adversarial nets
human paring project based on caffe tools
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Models
DomainBed is a suite to test domain generalization algorithms
A New Meta-Baseline for Few-Shot Learning
Few-shot Object Detection via Feature Reweighting
Code for the paper "Language Models are Unsupervised Multitask Learners"
A Machine Learning System for Data Enrichment.
[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4V. 接近GPT-4V表现的可商用开源多模态对话模型
Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
Inference code for LLaMA models
LLM training in simple, raw C/CUDA
Meta-Transformer for Unified Multimodal Learning
MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models
One Million Scenes for Autonomous Driving
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
A Comprehensive Toolkit for High-Quality PDF Content Extraction
PoolFormer: MetaFormer is Actually What You Need for Vision
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
The code of source-only training for our method
The proposed simulated dataset consisting of 9,536 charts and associated data annotations in CSV format.
A implementation of centerloss in multi_box_loss
(CVPR 2021) ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection