Computer Science and Programming

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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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Сеть:Computer Science and Programming Италия87 Технологии и приложения815...

📈 Аналитический обзор Telegram-канала Computer Science and Programming

Канал Computer Science and Programming (@computer_science_and_programming) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 142 757 подписчиков, занимая 815 место в категории Технологии и приложения и 87 место в регионе Италия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 142 757 подписчиков.

Согласно последним данным от 13 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -1 316, а за последние 24 часа — -26, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 6.13%. В первые 24 часа после публикации контент обычно набирает 1.79% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 8 753 просмотров. В течение первых суток публикация набирает 2 559 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 17.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, github, developer, pricing, waybienad.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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_sc...”

Благодаря высокой частоте обновлений (последние данные получены 14 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

142 757

Подписчики

-2624 часа

-1847 дней

-1 31630 день

8 753

Просмотры поста

~ 2 55924 часа

~ 3 64348 часов

6.13%

Коэффициент вовлеченности

~ 1

Постов в день

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Архив постов

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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

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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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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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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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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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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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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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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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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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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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Audio AI Timeline Here we will keep track of the latest AI models for audio generation, starting in 2023! ▪️SingSong: Generating musical accompaniments from singing - Paper ▪️AudioLDM: Text-to-Audio Generation with Latent Diffusion Models - Paper - Code ▪️Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion - Paper - Code ▪️Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models - Paper ▪️Noise2Music ▪️RAVE2 - Paper - Code ▪️MusicLM: Generating Music From Text - Paper ▪️Msanii: High Fidelity Music Synthesis on a Shoestring Budget - Paper - Code - HuggingFace ▪️ArchiSound: Audio Generation with Diffusion - Paper - Code ▪️VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers - Paper 👉@computer_science_and_programming

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