⏩ GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation



🖥 Github: https://github.com/chenhongyiyang/gpvit

➡️Paprer: https://arxiv.org/abs/2212.06795v1

✔️Data Preparation: https://paperswithcode.com/dataset/must-c

@Machine_learn

✅️ JRBD: Egocentric Perception of Humans


⭐️ Dataset: https://jrdb.erc.monash.edu/

🖥 Github: https://github.com/JRDB-dataset/jrdb_toolkit/

⏩ JRDB-Pose: https://jrdb.erc.monash.edu/dataset/pose#toolkit

✅ Paper: arxiv.org/pdf/1910.11792.pdf

@Machine_learn

🔥 Yolo8 is coming!


Github
Docs
Colab

@Machine_learn

⚡️ MVTN: Learning Multi-View Transformations for 3D Understanding



🖥Github: https://github.com/ajhamdi/mvtorch

⭐️ Paper: https://arxiv.org/abs/2212.13462v1

⏩ Dataset: https://paperswithcode.com/dataset/modelnet

⏩ Сlassification example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/classification.ipynb

➡️ Segmentation example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/segmentation.ipynb

@Machine_learn

Python Concurrency with asyncio Matthew Fowler
Matthew Fowler (2022)
#book #python 2022

@Machine_learn

When you are presenting a topic in the class and make eye contact with your friends😹😹😹
@Machine_learn

⭐️ The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation

🖥 Github: https://paperswithcode.com/paper/the-cropandweed-dataset-a-multi-modal

⏩ Paper: https://openaccess.thecvf.com/content/WACV2023/html/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.html

➡️ Datasets: https://paperswithcode.com/dataset/cropandweed-dataset

@Machine_learn

MEAP Edition
Manning Early Access Program
Math for Programmers
3D graphics, machine learning, and simulations with Python
Version 11
#book @Machine_learn

Multimodal Deep Learning
#book #DL #2023
@Machine_learn

30 Python Libraries to (Hugely) Boost Your Data Science Productivity
#Python
@Machine_learn

Build a Career in Data Science
EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
@Machine_learn

💬 GLIGEN: Open-Set Grounded Text-to-Image Generation

GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.


⭐️ Project: https://gligen.github.io/

⭐️ Demo: https://aka.ms/gligen

✅️ Paper: https://arxiv.org/abs/2301.07093

🖥 Github: https://github.com/gligen/GLIGEN

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AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling

Autoregressive approach for modeling dynamically deforming human bodies by Meta.


🖥 Github: github.com/facebookresearch/AutoAvatar

⭐️ Project: zqbai-jeremy.github.io/autoavatar

✅️ Paprer: arxiv.org/pdf/2203.13817.pdf

⏩ Dataset: https://amass.is.tue.mpg.de/index.html

⭐️ Video: https://zqbai-jeremy.github.io/autoavatar/static/images/video_arxiv.mp4

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🖥 Deep BCI SW ver. 1.0 is released.

🖥 Github: https://github.com/DeepBCI/Deep-BCI

⏩ Paper: https://arxiv.org/abs/2301.08448v1

➡️ Project: http://deepbci.korea.ac.kr/

@Machine_learn

✅️ StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis




🖥 Github: github.com/autonomousvision/stylegan-t

✅️ Paper: arxiv.org/pdf/2301.09515.pdf

⭐️ Project: sites.google.com/view/stylegan-t

✔️ Video: https://www.youtube.com/watch?v=MMj8OTOUIok&embeds_euri=https%3A%2F%2Fsites.google.com%2F&feature=emb_logo

🖥 Projected GAN: https://github.com/autonomousvision/projected-gan

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Pandas Basics
Oswald Campesato
#book #pandas #python
@Machne_learn

PACO: Parts and Attributes of Common Objects

🖥 Github
⭐️ Paper
➡️Project

@Machine_learn

❔ PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development



🖥 Github: https://github.com/primeqa/primeqa

🖥 Notebooks: https://github.com/primeqa/primeqa/tree/main/notebooks

✅️ Paper: https://arxiv.org/abs/2301.09715v2

⭐️ Dataset: https://paperswithcode.com/dataset/wikitablequestions

✔️ Docs: https://primeqa.github.io/primeqa/installation.html

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