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Computer Science and Programming

前往频道在 Telegram

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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网络:Computer Science and Programming 意大利88 技术与应用817...

📈 Telegram 频道 Computer Science and Programming 的分析概览

频道 Computer Science and Programming (@computer_science_and_programming) 英语语言赛道中的是活跃参与者。目前社区聚集了 138 848 名订阅者，在 技术与应用 类别中位列第 817，并在 意大利 地区排名第 88 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 138 848 名订阅者。

根据 01 十一月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -923，过去 24 小时变化为 -43，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 5.49%。内容发布后 24 小时内通常能获得 1.72% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 7 618 次浏览，首日通常累积 2 394 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 14。
主题关注点： 内容集中在 sellerflash, github, developer, pricing, waybienad 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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...”

凭借高频更新（最新数据采集于 02 十一月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

Buy Ad

138 848

订阅者

-4324 小时

-4127 天

-92330 天

7 618

帖子浏览量

~ 2 39424 小时

~ 3 43548 小时

5.49%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

138 848

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

138 848

You Only 👀 Once for Panoptic 🚙 Perception

138 848

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

138 848

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? 🙈

138 848

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

138 848

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

138 848

⚠ Message was hidden by channel owner

138 848

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

138 848

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

138 848

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

138 848

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)

138 848

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

138 848

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

138 848

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

138 848

It's CVPR 2021 time!

138 848

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

138 848

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

138 848

Paper: https://arxiv.org/pdf/2106.02034.pdf Github: https://github.com/raoyongming/DynamicViT Project page: https://dynamicvit.ivg-research.xyz/

138 848

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

138 848

Paper: https://people.engr.tamu.edu/nimak/Papers/SIGAsia2020_LF/resource/SIGGRAPH_2020_Light_Field_Authors_version.pdf Code: https://github.com/Turmac/light_field_synthesis Project page: https://people.engr.tamu.edu/nimak/Papers/SIGAsia2020_LF/index.html Dataset: https://drive.google.com/file/d/1oHywOJAAYm8YrSH5mDubjTAwN8jrCC9y/view