Computer Science and Programming – Telegram
Computer Science and Programming
152K subscribers
691 photos
29 videos
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978 links
Channel specialized for advanced topics of:
* Artificial intelligence,
* Machine Learning,
* Deep Learning,
* Computer Vision,
* Data Science
* Python

Admin: @otchebuch

Memes: @memes_programming

Ads: @Source_Ads,
https://telega.io/c/computer_science
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YOLOv8 is the newest state-of-the-art YOLO model that can be used for object detection, image classification, and instance segmentation tasks. YOLOv8 includes numerous architectural and developer experience changes and improvements over YOLOv5.

Code:
https://github.com/ultralytics/ultralytics

What's New in YOLOv8 ?
https://blog.roboflow.com/whats-new-in-yolov8/

Yolov8 Instance Segmentation (ONNX):
https://github.com/ibaiGorordo/ONNX-YOLOv8-Instance-Segmentation

👉 @computer_science_and_programming
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Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution

BoxInstSeg
is a toolbox that aims to provide state-of-the-art box-supervised instance segmentation algorithms. It supports instance segmentation with only box annotations.


Github:
https://github.com/LiWentomng/BoxInstSeg

Paper:
https://arxiv.org/pdf/2212.01579.pdf

👉@computer_science_and_programming
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GLIGEN: Open-Set Grounded Text-to-Image Generation.

GLIGEN
(Grounded-Language-to-Image Generation) a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs.

Project page:
https://gligen.github.io/

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

Github (coming soon):
https://github.com/gligen/GLIGEN

Demo:
https://huggingface.co/spaces/gligen/demo


👉@computer_science_and_programming
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Cut and Learn for Unsupervised Object Detection and Instance Segmentation

Cut-and-LEaRn
(CutLER) is a simple approach for training object detection and instance segmentation models without human annotations. It outperforms previous SOTA by 2.7 times for AP50 and 2.6 times for AR on 11 benchmarks.

Paper:
https://arxiv.org/pdf/2301.11320.pdf

Github:
https://github.com/facebookresearch/CutLER

Demo:
https://colab.research.google.com/drive/1NgEyFHvOfuA2MZZnfNPWg1w5gSr3HOBb?usp=sharing

👉@computer_science_and_programming
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Gen-1: The Next Step Forward for Generative AI

Use words and images to generate new videos out of existing

Introducing Gen-1: a new AI model that uses language and images to generate new videos out of existing ones.

https://research.runwayml.com/gen1

⭐️ Project:
https://research.runwayml.com/gen1

Paper:
https://arxiv.org/abs/2302.03011

📌Request form:
https://docs.google.com/forms/d/e/1FAIpQLSfU0O_i1dym30hEI33teAvCRQ1i8UrGgXd4BPrvBWaOnDgs9g/viewform

👉@computer_science_and_programming
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YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection

SPATIO-temporal action detection (STAD) aims to detect action instances in the current frame, which it has been widely applied, such as video surveillance and somatosensory game.

Paper:
https://arxiv.org/pdf/2302.06848.pdf

Github:
https://github.com/yjh0410/YOWOv2

Dataset:
https://drive.google.com/file/d/1Dwh90pRi7uGkH5qLRjQIFiEmMJrAog5J/view?usp=sharing

👉@computer_science_and_programming
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3D-aware Conditional Image Synthesis (pix2pix3D)

Pix2pix3D
synthesizes 3D objects (neural fields) given a 2D label map, such as a segmentation or edge map

Github:
https://github.com/dunbar12138/pix2pix3D

Paper:
https://arxiv.org/abs/2302.08509

Project:
https://www.cs.cmu.edu/~pix2pix3D/

Datasets:
CelebAMask , AFHQ-Cat-Seg , Shapenet-Car-Edge


👉@computer_science_and_programming
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Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

Efficient Teacher introduces semi-supervised object detection into practical applications, enabling users to obtain a strong generalization capability with only a small amount of labeled data and large amount of unlabeled data.

Efficient Teacher provides category and custom uniform sampling, which can quickly improve the network performance in actual business scenarios.


Paper:
https://arxiv.org/abs/2302.07577

Github:
https://github.com/AlibabaResearch/efficientteacher

👉@computer_science_and_programming
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Multivariate Probabilistic Time Series Forecasting with Informer

Efficient transformer-based model for LSTF.

Method introduces a Probabilistic Attention mechanism to select the “active” queries rather than the “lazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.

🤗Hugging face:
https://huggingface.co/blog/informer

Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer

⭐️ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb

💨 Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform

👉@computer_science_and_programming
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ViperGPT: Visual Inference via Python Execution for Reasoning

ViperGPT,
a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.


Github:
https://github.com/cvlab-columbia/viper

Paper:
https://arxiv.org/pdf/2303.08128.pdf

Project:
https://paperswithcode.com/dataset/beat

👉@computer_science_and_programming
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Test of Time: Instilling Video-Language Models with a Sense of Time

GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models.

Paper:
https://arxiv.org/abs/2301.02074

Code:
https://github.com/bpiyush/TestOfTime

Project Page:
https://bpiyush.github.io/testoftime-website/

👉 @computer_science_and_programming
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DragGAN.gif
20.6 MB
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Paper:
https://arxiv.org/abs/2305.10973

Github:
https://github.com/XingangPan/DragGAN

Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/

👉 @computer_science_and_programming
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🔭 GRES: Generalized Referring Expression Segmentation

New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects.

🖥 Github: https://github.com/henghuiding/ReLA

Paper: https://arxiv.org/abs/2306.00968

🔎 Project: https://henghuiding.github.io/GRES/

📌 New dataset: https://github.com/henghuiding/gRefCOCO

👉 @computer_science_and_programming
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80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife

Classification library
https://github.com/Tessellate-Imaging/monk_v1

Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo

Detection and Segmentation Library
https://github.com/Tessellate-Imaging/

Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

👉 @computer_science_and_programming
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𝗛𝗼𝘄 𝘁𝗼 𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗔𝗣𝗜𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗩𝗶𝘀𝘂𝗮𝗹 𝗦𝘁𝘂𝗱𝗶𝗼 𝗖𝗼𝗱𝗲?

You can immediately do this from your Visual Studio Code, as Postman just released a VS Code extension that integrates API building and testing into your code editor.

What you can do with the extension:

🔹𝗦𝗲𝗻𝗱 (𝗺𝘂𝗹𝘁𝗶𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹) 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀
🔹𝗦𝗲𝗻𝗱 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂𝗿 𝗵𝗶𝘀𝘁𝗼𝗿𝘆
🔹𝗨𝘀𝗲 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀
🔹𝗨𝘀𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀
🔹𝗩𝗶𝗲𝘄 𝗮𝗻𝗱 𝗲𝗱𝗶𝘁 𝗰𝗼𝗼𝗸𝗶𝗲𝘀

➡️Check it here
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Backend Burger 🍔
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Wondering how C++, Java, Python Work?

🔵 C++
C++ is like the superhero of programming languages. It's a compiled language, meaning your code is transformed into machine code that your computer can understand before it runs. This compilation process is crucial for efficiency and performance. C++ gives you precise control over memory and hardware, making it a top choice for systems programming and game development. It's like wielding a finely-tuned instrument in the world of code! 🎸💻

🔴 Java
Java, on the other hand, is the coffee of programming languages. It's a compiled language too but with a twist. Java code is compiled into bytecode, which runs on the Java Virtual Machine (JVM). This bytecode can run on any platform with a compatible JVM, making Java highly portable and platform-independent. It's a bit like sending your code to a virtual coffee machine that serves it up just the way you like it on any OS! ☕️💼

🐍 Python
Python is the friendly neighborhood programming language. It's an interpreted language, which means there's no compilation step. Python code is executed line by line by the Python interpreter. This simplicity makes it great for beginners and rapid development. Python's extensive library ecosystem and easy syntax make it feel like you're noscripting magic spells in a magical world! 🪄🐍

In the end, the choice of programming language depends on your project's needs and your personal preferences. Each language has its strengths and weaknesses, but they all share the goal of bringing your ideas to life through code. 🚀💡

So, whether you're crafting the perfect C++ masterpiece, brewing up Java applications, or noscripting Python magic, remember that programming languages are the tools that empower us to create amazing things in the digital realm. Embrace the language that speaks to you and keep coding! 💻🌟
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