Vision CAIR Group, KAUST, supported by Mohamed Elhoseiny
39
Public repositories
28,095
Total stars
598
Followers
Vision CAIR Research Group, KAUST, maintains a significant public presence on GitHub, showcasing a wide range of repositories primarily in Python, Jupyter Notebook, HTML, and JavaScript. Notable projects include MiniGPT-4 and ChatCaptioner, which contribute to advancements in video understanding and image captioning.
Top languages
Public repositories
MiniGPT-4
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
MiniGPT4-video
Official code for Goldfish model for long video understanding and MiniGPT4-video for short video understanding
ChatCaptioner
Official Repository of ChatCaptioner
LongVU
[ICML 2025] Official PyTorch implementation of LongVU
VisualGPT
VisualGPT, CVPR 2022 Proceeding, GPT as a decoder for vision-language models
MiniGPT-Med
Open-sourced code of MiniGPT-Med
3DCoMPaT-v2
3DCoMPaT++: An improved large-scale 3D vision dataset for compositional recognition
MammalNet
No description provided for this repository.
LTVRR
No description provided for this repository.
artemis-v2
Code for the paper: It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection
RelTransformer
No description provided for this repository.
dochaystacks
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents, CVPR 2025
Infinibench
Official InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows
3DCoMPaT
Official repository for the 3DCoMPaT dataset (ECCV2022 Oral)
iMotion-LLM
No description provided for this repository.
affectiveVisDial
No description provided for this repository.
saai-factory-tutorial-creative-ai
Creative AI for Visual Art and Music slides and demos.
AF-Guide
Official repository of Action-Free Guide
CWAN
Creative Walk Adversarial Networks: Novel Art Generation with Probabilistic Random Walk Deviation from Style Norms
Freshness-Aware-PER
No description provided for this repository.
HalentNet
No description provided for this repository.
CIZSLv2
CIZSL++: Creativity Inspired Generative Zero-Shot Learning. T-PAMI under review.
WAGA
Code for Wölfflin Affective Generative Analysis paper published in ICCC 2021
cs326-few-shot-classification
CS326 Practical assignment #2: few-shot classification
GRaWD
Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation. CVPR 2022 Workshop, ICCC 2022.
artelingo
No description provided for this repository.
UnlikelihoodMotionForecasting
No description provided for this repository.
HomeGPT
No description provided for this repository.
ROLL
An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models
Goldfish_website
No description provided for this repository.
3dreftransformer
No description provided for this repository.
FishNet2.0
No description provided for this repository.
affective-vision-language
No description provided for this repository.
Zero-Shot-Learning
VisionCAIR Zero-Shot Learning Research
Affective-and-Creative-AI
VisionCAIR Affective and Creative AI Research
lifelong_fact_learning
No description provided for this repository.
feelings
No description provided for this repository.
CIZSL
Creativity Inspired Zero-Shot Learning
GDPP
Generator loss to reduce mode-collapse and to improve the generated samples quality.
Frequently asked questions
What does Vision-CAIR build on GitHub?
Vision-CAIR builds various projects on GitHub, focusing on artificial intelligence and machine learning. Their repositories include MiniGPT-4 for natural language processing and ChatCaptioner for caption generation, among others.
Which programming languages does Vision-CAIR use?
Vision-CAIR primarily uses Python and Jupyter Notebook for their projects, along with HTML and JavaScript. This diverse language usage supports their work in AI, machine learning, and data visualization.
Are Vision-CAIR's repositories public?
Yes, all of Vision-CAIR's repositories are public on GitHub. This openness allows other researchers and developers to access, use, and contribute to their innovative projects in the field of AI.
Is this exposure intended?
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