⏩ PODA: Prompt-driven Zero-shot Domain Adaptation
.

🖥 Github: https://github.com/astra-vision/poda

⏩ Paprer: https://arxiv.org/abs/2212.03241v1

❤️ Pretrainde model: https://drive.google.com/drive/folders/15-NhVItiVbplg_If3HJibokJssu1NoxL?usp=share_link

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

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🔼 IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation

🖥 Github: https://github.com/shendu0321/incepformer

✔️ Project: https://github.com/shendu0321/IncepFormer

🗒 Paper: https://arxiv.org/abs/2212.03035v1

➡️ Data: https://paperswithcode.com/dataset/cityscapes

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•(Multi-Modal Image Fusion)
。(nfrared and visible image fusion)
。 (Medical image fusion)
•(Digital Photography Image Fusion)
。(Multi-exposure image fusion)
。(Multi-focus image fusion)
• (Remote Sensing Image Fusion)
。(Pansharpening)
•(General Image Fusion Framerwork)
#(Survey)
#(Dataset)
#(Evaluation Metric)
#(General evaluation metric
github.com/miao19980215/Image-Fusion


@Machine_learn

✅ pypop7 (Pure-PYthon library of POPulation-based black-box OPtimization)



$ pip install pypop7

🖥 Github: https://github.com/evolutionary-intelligence/pypop

⏩ Paprer: https://arxiv.org/abs/2212.05652v1

⭐️ Derivative-Free Optimization (DFO): https://link.springer.com/article/10.1007/s10208-021-09513-z

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✔️ ECON: Explicit Clothed humans Obtained from Normals



🖥 Github: https://github.com/YuliangXiu/ECON

⏩ Paprer: https://arxiv.org/abs/2212.07422

📎 Demo: https://github.com/YuliangXiu/ECON#demo

✔️ Instructions: https://github.com/YuliangXiu/ECON#instructions

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✅ DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

git clone --recurse-submodules git@github.com:cvg/DeepLSD.git
cd DeepLSD

🖥 Github: https://github.com/cvg/deeplsd

⏩ Paprer: https://arxiv.org/abs/2212.07766v1

✔️ Dataset: https://paperswithcode.com/dataset/hpatches

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⏩ 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

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✅️ 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

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🔥 Yolo8 is coming!


Github
Docs
Colab

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⚡️ 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

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

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

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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
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30 Python Libraries to (Hugely) Boost Your Data Science Productivity
#Python
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Build a Career in Data Science
EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
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💬 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

@Machine_learn