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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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Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 142 757 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 815-o'rinni va Italiya mintaqasida 87-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 142 757 obunachiga ega bo‘ldi.

13 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -1 316 ga, so‘nggi 24 soatda esa -26 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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Yuqori yangilanish chastotasi (oxirgi ma’lumot 14 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

142 757

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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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