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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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.

📊 Auditoriya ko‘rsatkichlari va dinamika

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.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 6.13% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.79% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 8 753 marta ko‘riladi; birinchi sutkada odatda 2 559 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 17 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent sellerflash, github, developer, pricing, waybienad kabi asosiy mavzularga jamlangan.

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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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...”

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

Obunachilar

-2624 soatlar

-1847 kunlar

-1 31630 kunlar

8 753

Post ko'rishlar

~ 2 55924 soatlar

~ 3 64348 soatlar

6.13%

Muloqot nisbati

~ 1

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Postlar arxiv

142 757

UFO: segmentation 140+ FPS 👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one! 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Unified framework for co-segmentation ✅Co-segmentation, co-saliency, saliency ✅Block for long-range dependencies ✅Able to reach for 140 FPS in inference ✅The new SOTA on multiple datasets Paper: https://arxiv.org/pdf/2203.04708v2.pdf Code: https://github.com/suyukun666/UFO

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Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation. Github: https://github.com/164140757/scm Paper: https://arxiv.org/abs/2207.10447v1 Dataset: https://paperswithcode.com/dataset/cub-200-2011

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🏎 Instance Shadow Detection with A Single-Stage Detector Deep framework, and an evaluation metric to approach this new task. Github: https://github.com/stevewongv/InstanceShadowDetection Instance Shadow Detection: https://github.com/stevewongv/SSIS Video: https://www.youtube.com/watch?v=p0b_2SsFypw Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing Paper: https://arxiv.org/abs/2207.04614v1 Datasets: https://paperswithcode.com/dataset/soba @computer_science_and_programming

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Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models. Github: https://github.com/DigitalPhonetics/IMS-Toucan https://github.com/rballester/tntorch Pre-Generated Audios: https://multilingualtoucan.github.io/ Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/ Interactive Demo: https://huggingface.co/spaces/Flux9665/IMS-Toucan Paper: https://arxiv.org/abs/2206.12229v1 @computer_science_and_programming

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CVPR 2022 open access https://openaccess.thecvf.com/CVPR2022?day=2022-06-21

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MIT, Introduction to Deep Learning, 2022 Lecture series Website: http://introtodeeplearning.com/ Lecture: https://www.youtube.com/watch?v=7sB052Pz0sQ&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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Squeezeformer: An Efficient Transformer for Automatic Speech Recognition Github: https://github.com/kssteven418/squeezeformer Paper: https://arxiv.org/abs/2206.00888v1 Dataset: https://paperswithcode.com/dataset/librispeech

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AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition Github: https://github.com/ShoufaChen/AdaptFormer Paper: https://arxiv.org/abs/2205.13535v1 Dataset: https://paperswithcode.com/dataset/something-something-v2

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RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features (CVPR 2021) Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_RefineMask_Towards_High-Quality_Instance_Segmentation_With_Fine-Grained_Features_CVPR_2021_paper.pdf Source: https://github.com/zhanggang001/RefineMask @computer_science_and_programming

142 757

🧊 Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral) Github: https://github.com/dvlab-research/focalsconv Paper: https://arxiv.org/abs/2204.12463 Dataset: https://paperswithcode.com/dataset/nuscenes

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NAFSSR: Stereo Image Super-Resolution Using NAFNet Github: https://github.com/megvii-research/NAFNet Paper: https://arxiv.org/abs/2204.08714v1 Demo: https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing Dataset: https://paperswithcode.com/dataset/kitti

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💬 A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution Github: https://github.com/mjq11302010044/tatt Paper: https://arxiv.org/abs/2203.09388v2 Dataset: https://deepchecks.com/blog/

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Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science. https://towardsdatascience.com/ai-papers-to-read-in-2022-c6edd4302247

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A lightweight vision library for performing large scale object detection & instance segmentation Github: https://github.com/obss/sahi Paper: https://arxiv.org/abs/2202.06934v1 Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx Dataset: https://paperswithcode.com/dataset/xview 👉👉@computer_science_and_programming

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323+ Open Source Pytorch Implementation Software Projects Free and open source pytorch implementation code projects including engines, APIs, generators, and tools. https://opensourcelibs.com/libs/pytorch-implementation A curated list of tutorials, papers, projects, communities and more related to PyTorch: https://www.ritchieng.com/the-incredible-pytorch/ https://github.com/ritchieng/the-incredible-pytorch @computer_science_and_programming

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✨ Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning Github: https://github.com/sense-x/uniformer Paper: https://arxiv.org/abs/2201.04676v1 Tasks: https://paperswithcode.com/dataset/kinetics-600 @computer_science_and_programming

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Happy new year Thank you for being with us We appreciate your patience to science and always try to provide best content for subscribers

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Page: https://d2l.ai/ PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf MXNET based: https://d2l.ai/d2l-en.pdf Github: https://github.com/d2l-ai/d2l-en 👉👉@computer_science_and_programming

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Dive into Deep Learning Interactive deep learning book with code, math, and discussions Implemented with NumPy/MXNet, PyTorch, and TensorFlow Adopted at 300 universities from 55 countries