اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Computer Science and Programming
رفتن به کانال در 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
نمایش بیشتر📈 تحلیل کانال تلگرام Computer Science and Programming
کانال Computer Science and Programming (@computer_science_and_programming) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 142 757 مشترک است و جایگاه 815 را در دسته فناوری و برنامهها و رتبه 87 را در منطقه ايطاليا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 142 757 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 13 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -1 316 و در ۲۴ ساعت گذشته برابر -26 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 6.13% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.79% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 8 753 بازدید دریافت میکند. در اولین روز معمولاً 2 559 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 17 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند sellerflash, github, developer, pricing, waybienad تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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...”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 14 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
142 757
مشترکین
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-1847 روز
-1 31630 روز
آرشیو پست ها
80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife
Classification library
https://github.com/Tessellate-Imaging/monk_v1
Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo
Detection and Segmentation Library
https://github.com/Tessellate-Imaging/
Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo
🔭 GRES: Generalized Referring Expression Segmentation
New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects.
🖥 Github: https://github.com/henghuiding/ReLA
⏩ Paper: https://arxiv.org/abs/2306.00968
🔎 Project: https://henghuiding.github.io/GRES/
📌 New dataset: https://github.com/henghuiding/gRefCOCO
👉 @computer_science_and_programming
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Paper:
https://arxiv.org/abs/2305.10973
Github:
https://github.com/XingangPan/DragGAN
Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/
👉 @computer_science_and_programming
Test of Time: Instilling Video-Language Models with a Sense of Time
GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models.
Paper:
https://arxiv.org/abs/2301.02074
Code:
https://github.com/bpiyush/TestOfTime
Project Page:
https://bpiyush.github.io/testoftime-website/
👉 @computer_science_and_programming
ViperGPT: Visual Inference via Python Execution for Reasoning
ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.
Github:
https://github.com/cvlab-columbia/viper
Paper:
https://arxiv.org/pdf/2303.08128.pdf
Project:
https://paperswithcode.com/dataset/beat
👉@computer_science_and_programming
+1Multivariate Probabilistic Time Series Forecasting with Informer
Efficient transformer-based model for LSTF.
Method introduces a Probabilistic Attention mechanism to select the “active” queries rather than the “lazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
🤗Hugging face:
https://huggingface.co/blog/informer
⏩ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer
⭐️ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb
💨 Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform
👉@computer_science_and_programming
+2Efficient Teacher: Semi-Supervised Object Detection for YOLOv5
✅ Efficient Teacher introduces semi-supervised object detection into practical applications, enabling users to obtain a strong generalization capability with only a small amount of labeled data and large amount of unlabeled data.
✅ Efficient Teacher provides category and custom uniform sampling, which can quickly improve the network performance in actual business scenarios.
Paper:
https://arxiv.org/abs/2302.07577
Github:
https://github.com/AlibabaResearch/efficientteacher
👉@computer_science_and_programming
3D-aware Conditional Image Synthesis (pix2pix3D)
Pix2pix3D synthesizes 3D objects (neural fields) given a 2D label map, such as a segmentation or edge map
Github:
https://github.com/dunbar12138/pix2pix3D
Paper:
https://arxiv.org/abs/2302.08509
Project:
https://www.cs.cmu.edu/~pix2pix3D/
Datasets:
CelebAMask , AFHQ-Cat-Seg , Shapenet-Car-Edge
👉@computer_science_and_programming
YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection
SPATIO-temporal action detection (STAD) aims to detect action instances in the current frame, which it has been widely
applied, such as video surveillance and somatosensory game.
Paper:
https://arxiv.org/pdf/2302.06848.pdf
Github:
https://github.com/yjh0410/YOWOv2
Dataset:
https://drive.google.com/file/d/1Dwh90pRi7uGkH5qLRjQIFiEmMJrAog5J/view?usp=sharing
👉@computer_science_and_programming
Gen-1: The Next Step Forward for Generative AI
Use words and images to generate new videos out of existing
Introducing Gen-1: a new AI model that uses language and images to generate new videos out of existing ones.
https://research.runwayml.com/gen1
⭐️ Project:
https://research.runwayml.com/gen1
✅ Paper:
https://arxiv.org/abs/2302.03011
📌Request form:
https://docs.google.com/forms/d/e/1FAIpQLSfU0O_i1dym30hEI33teAvCRQ1i8UrGgXd4BPrvBWaOnDgs9g/viewform
👉@computer_science_and_programming
Audio AI Timeline
Here we will keep track of the latest AI models for audio generation, starting in 2023!
▪️SingSong: Generating musical accompaniments from singing
- Paper
▪️AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
- Paper
- Code
▪️Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
- Paper
- Code
▪️Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
- Paper
▪️Noise2Music
▪️RAVE2
- Paper
- Code
▪️MusicLM: Generating Music From Text
- Paper
▪️Msanii: High Fidelity Music Synthesis on a Shoestring Budget
- Paper
- Code
- HuggingFace
▪️ArchiSound: Audio Generation with Diffusion
- Paper
- Code
▪️VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
- Paper
👉@computer_science_and_programming
