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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

Red:Computer Science and Programming Italia88 Tecnologías y Aplicaciones816...

📈 Análisis del canal de Telegram Computer Science and Programming

El canal Computer Science and Programming (@computer_science_and_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 138 878 suscriptores, ocupando la posición 816 en la categoría Tecnologías y Aplicaciones y el puesto 88 en la región Italia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 138 878 suscriptores.

Según los últimos datos del 31 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -883, y en las últimas 24 horas de -66, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.88%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.72% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 10 942 visualizaciones. En el primer día suele acumular 2 393 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 25.
Intereses temáticos: El contenido se centra en temas clave como sellerflash, github, developer, pricing, waybienad.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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...”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 01 noviembre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

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138 878

Suscriptores

-6624 horas

-4227 días

-88330 días

10 942

Visitas de la publicación

~ 2 39324 horas

~ 3 43548 horas

7.88%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

138 862

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

138 862

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

138 862

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138 862

🔗 Link:- https://apitester.org A fully free mobile API client for interacting with APIs straight from your phone. Doesn't it sound fantastic? API Tester allows you to connect to whatever type of API you're working with, including REST, gRPC, SOAP, and GraphQL. Constructing HTTP requests with parameters, auth details, and body data requires only a few steps with a simple and optimized UI. You can also create WebSocket connections, import collections, and use global variables. API Tester was developed by a team of enthusiasts who feel that powerful apps simplifying work is the key to progress. The app is constantly updated to ensure that you have all of the top-tier features. Try it out for yourself, rate and review on the App Store and Google Play.

138 862

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

138 862

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

138 862

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

138 862

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

138 862

MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications. 2023 lectures are starting in just one day, Jan 9th! http://introtodeeplearning.com MIT Introduction to Deep Learning The 2022 lectures can be found here: https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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PACO: Parts and Attributes of Common Objects Sometimes object detection is not enough and you need more detail about object. Especially, when parts of objects is matters in your task. Then this dataset is for you from Facebook research team. PACO is a detection dataset that goes beyond traditional object boxes and masks and provides richer annotations such as part masks and attributes. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. Paper: https://arxiv.org/pdf/2301.01795.pdf Github: https://github.com/facebookresearch/paco Visualization: https://github.com/facebookresearch/paco/tree/main/notebooks

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Happy New Year! In the new 2023 year, we wish a rapid increase in subscribers, high posts reach, high-quality active audience and, of course, happiness and health. A traditional present from us is a New Year card with your channel's this year results. See you in 2023, from TGSTAT team

138 862

Accurate and Efficient Stereo Matching via Attention Concatenation Volume Stereo Depth Estimation Paper: https://arxiv.org/pdf/2209.12699.pdf Github: https://github.com/gangweiX/Fast-ACVNet Demo: https://www.youtube.com/watch?v=az4Z3dp72Zw ONNX: ONNX-FastACVNet-Stereo-Depth-Estimation @computer_science_and_programming

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DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients DeepLSD is a generic line detector that combines the robustness of deep learning with the accuracy of handcrafted detectors. It can be used to extract generic line segments from images in-the-wild, and is suitable for any task requiring high precision, such as homography estimation, visual localization, and 3D reconstruction. By predicting a line distance and angle fields, it can furthermore refine any existing line segments through an optimization Paper: https://arxiv.org/abs/2212.07766v1 Github: https://github.com/cvg/deeplsd Dataset: https://paperswithcode.com/dataset/hpatches

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DiffusionInst: Diffusion Model for Instance Segmentation * DiffusionInst is the first work of diffusion model for instance segmentation Github: https://github.com/chenhaoxing/DiffusionInst Paper: https://arxiv.org/abs/2212.02773v2 Getting started: https://github.com/chenhaoxing/DiffusionInst/blob/main/GETTING_STARTED.md Dataset: https://paperswithcode.com/dataset/lvis

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Automatically find and fix errors in any ML datasets with cleanlab This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training. Github: https://github.com/cleanlab/cleanlab @computer_science_and_programming Docs: https://docs.cleanlab.ai/stable/index.html Examples: https://github.com/cleanlab/examples Paper: https://arxiv.org/abs/2211.13895v1

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SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation The dataset consists of unlabeled patch triplets from 251079 locations across the globe, each patch covering 2640mx2640m and including 4 seasonal time stamps. Github: https://github.com/zhu-xlab/ssl4eo-s12 Paper: https://arxiv.org/abs/2211.07044v1 Dataset: https://mediatum.ub.tum.de/1660427 @computer_science_and_programming