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87 技术与应用816...

📈 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 142 737 名订阅者，在 技术与应用 类别中位列第 816，并在 意大利 地区排名第 87 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 142 737 名订阅者。

根据 14 六月, 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 -1 292，过去 24 小时变化为 -44，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 6.29%。内容发布后 24 小时内通常能获得 1.82% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 8 976 次浏览，首日通常累积 2 595 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 17。
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 15 六月, 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。

142 737

订阅者

-4424 小时

-2007 天

-1 29230 天

8 976

帖子浏览量

~ 2 59524 小时

~ 3 64748 小时

6.29%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

142 737

Advancing the state of the art in computer vision with self-supervised Transformers and 10x more efficient training

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CS224W: Machine Learning with Graphs - Stanford / Winter 2021 https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW Full Stack Deep Learning - Spring 2021 - UC Berkeley https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9 Introduction to Deep Learning (I2DL) - Technical University of Munich https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk 3D Computer Vision - National University of Singapore - 2021 https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ CV3DST - Computer Vision 3: Detection, Segmentation and Tracking https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe ADL4CV - Advanced Deep Learning for Computer Vision https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz

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2021- Courses List of Machine Learning, Deep Learning, and Computer Vision from a top school

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Paper: https://arxiv.org/pdf/1811.08264.pdf Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network Learning list: https://github.com/DirtyHarryLYL/HOI-Learning-List

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Transferable Interactiveness Knowledge forHuman-Object Interaction Detection

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Paper: https://arxiv.org/pdf/2104.01604v1.pdf Github: https://github.com/speechbrain/speechbrain/tree/develop/recipes/timers-and-such Dataset: https://zenodo.org/record/4623772#.YHFLkxKRWJk

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Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers End-to-end pipeline for Spoken Language Understanding (SLU)

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Github: https://github.com/ai-coodinator/yolact_edge YolactEdge, competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images.

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YolactEdge Real time Instance Segmentation on the Edge https://www.youtube.com/watch?v=pMDwXkIerw8

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Paper: https://arxiv.org/pdf/2103.07941v1.pdf Github: https://github.com/hkchengrex/MiVOS Project page: https://hkchengrex.github.io/MiVOS/

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CVPR 2021 paper Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion (MiVOS)

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Blog: https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/ Github: https://github.com/facebookresearch/detectron2 Documentation: https://detectron2.readthedocs.io/en/latest/tutorials/getting_started.html Colab tutorial: https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5#scrollTo=QHnVupBBn9eR

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Detectron2 is a ground-up rewrite of Detectron that started with maskrcnn-benchmark. The platform is now implemented in PyTorch.

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Paper: https://arxiv.org/pdf/2102.09480.pdf Github: https://ycliu93.github.io/projects/unbiasedteacher.html Project page: https://ycliu93.github.io/projects/unbiasedteacher.html

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"Unbiased Teacher for Semi-Supervised Object Detection" from Facebook researchers

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Github: https://github.com/mobvoi/wenet Paper: https://arxiv.org/pdf/2102.01547v1.pdf Tutorial: https://github.com/mobvoi/wenet/blob/main/docs/tutorial.md

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End-to-end speech recognition toolkit (based on Pytorch) What's make it different from other ASR toolkits: * Production first and production ready * Unified solution for streaming and non-streaming ASR * Portable runtime * Light weight

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Github: https://github.com/tohinz/CharacterGAN Paper: https://arxiv.org/pdf/2102.03141.pdf You can use also interactive GUI to easily repose a given character based on keypoints.

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Create Character Animation with small amount of data by using Generative Adversarial Network (GAN)

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Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network Paper: https://arxiv.org/pdf/2101.10292v1.pdf Human-Object Interaction Learning studies (bonus): https://github.com/DirtyHarryLYL/HOI-Learning-List