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 Italy88 Technologies & Applications804...

📈 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 140 441 subscribers, ranking 804 in the Technologies & Applications category and 88 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 140 441 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -734 over the last 30 days and by -18 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.96%. Within the first 24 hours after publication, content typically collects 1.94% reactions from the total number of subscribers.
Post reach: On average, each post receives 11 177 views. Within the first day, a publication typically gains 2 728 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
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 01 September, 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.

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140 441

Subscribers

-1824 hours

-2617 days

-73430 days

11 177

Post views

~ 2 72824 hours

~ 3 75748 hours

7.96%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

140 432

Unseen Object Amodal Instance Segmentation (UOAIS)

140 432

Paper (ICCV2021): https://arxiv.org/pdf/2109.04425v1.pdf Github: https://github.com/yumingj/Talk-to-Edit Dataset: https://drive.google.com/drive/folders/18nejI_hrwNzWyoF6SW8bL27EYnM4STAs 👉@computer_science_and_programming

140 432

Now we can generate the faces with just with talking

140 432

Paper: https://arxiv.org/pdf/2108.11250v1.pdf Github: https://github.com/hustvl/YOLOP

140 432

You Only 👀 Once for Panoptic 🚙 Perception

140 432

Paper: https://arxiv.org/pdf/2105.06993.pdf Github: https://github.com/erikalu/omnimatte Project Page: https://omnimatte.github.io/ Supplimentary material: https://omnimatte.github.io/supplementary/index.html Explained: https://www.youtube.com/watch?v=lCBSGOwV-_o 👉@computer_science_and_programming

140 432

Now removing, duplicating or enhancing objects in video is more realistic with the assist of AI "We need to talk about the car in the room." This paper: what car? 🙈

140 432

Paper: https://arxiv.org/pdf/2103.14030.pdf Github: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection Demo: https://www.youtube.com/watch?v=FQVS_0Bja6o 👉@computer_science_and_programming

140 432

Swin transformer for : ✔️ Object detection ✔️ Image Classification ✔️ Semantic Segmentation ✔️ Video Recognition

140 432

⚠ Message was hidden by channel owner

140 432

Paper: https://arxiv.org/pdf/2107.10833v1.pdf Github: https://github.com/xinntao/Real-ESRGAN Colab demo: https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing 👉@computer_science_and_programming

140 432

Practical image restoration Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data 👉@computer_science_and_programming

140 432

A simpler design but better performance! It aims to bridge the gap between research and industrial communities. Paper: https://arxiv.org/pdf/2107.08430v1.pdf Github: https://github.com/Megvii-BaseDetection/YOLOX 👉@computer_science_and_programming

140 432

YOLOX: Exceeding YOLO Series in 2021 Anchor-free version of YOLO series Won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021)

140 432

SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping Paper: https://arxiv.org/pdf/2105.07014.pdf Video: https://www.youtube.com/watch?v=W7NCbfZp6QE Code: https://github.com/google-research/google-research/tree/master/smurf 👉 @computer_science_and_programming

140 432

From Google and Waymo researchers: The self-/unsupervised revolution is near! Unsupervised optical flow model SMURF improves SOTA by 40% and beats many supervised methods such as PWC-Net and FlowNet2 👉 @computer_science_and_programming

140 432

All Open Access Papers: https://openaccess.thecvf.com/CVPR2021

140 432

It's CVPR 2021 time!

140 432

Paper: https://arxiv.org/pdf/2106.13797v2.pdf Github: https://github.com/whai362/PVT

140 432

PVTv2: Improved Baselines with Pyramid Vision Transformer ✅ Classification ✅ Detection ✅ Segmentation