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

Network:Computer Science and Programming Italy87 Technologies & Applications815...

📈 Analytical overview of Telegram channel Computer Science and Programming

Channel Computer Science and Programming (@computer_science_and_programming) in the English language segment is an active participant. Currently, the community unites 142 757 subscribers, ranking 815 in the Technologies & Applications category and 87 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 142 757 subscribers.

According to the latest data from 13 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -1 316 over the last 30 days and by -26 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.13%. Within the first 24 hours after publication, content typically collects 1.79% reactions from the total number of subscribers.
Post reach: On average, each post receives 8 753 views. Within the first day, a publication typically gains 2 559 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 17.
Thematic interests: Content is focused on key topics such as sellerflash, github, developer, pricing, waybienad.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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...”

Thanks to the high frequency of updates (latest data received on 14 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

142 757

Subscribers

-2624 hours

-1847 days

-1 31630 days

8 753

Post views

~ 2 55924 hours

~ 3 64348 hours

6.13%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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

142 757

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

142 757

🏎 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

142 757

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

142 757

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

142 757

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

142 757

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

142 757

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

142 757

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

142 757

💬 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/

142 757

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

142 757

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

142 757

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

142 757

✨ 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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142 757

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

142 757

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

142 757

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