AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/ #AI #chatGPT
Show more📈 Analytical overview of Telegram channel AI with Papers - Artificial Intelligence & Deep Learning
Channel AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) in the English language segment is an active participant. Currently, the community unites 17 262 subscribers, ranking 7 543 in the Technologies & Applications category and 2 144 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 262 subscribers.
According to the latest data from 26 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 250 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 17.43%. Within the first 24 hours after publication, content typically collects 7.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 008 views. Within the first day, a publication typically gains 1 216 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 16.
- Thematic interests: Content is focused on key topics such as framework, object, dataset, tba, depth.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision
Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
#AI #chatGPT”
Thanks to the high frequency of updates (latest data received on 27 October, 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.
17 262
Subscribers
+124 hours
+97 days
+25030 days
Posts Archive
🌲 MagicEdit: Magic Video Editing 🌲
👉MagicEdit: explicit disentangling the learning of content, structure & motion for Hi-Fi and temporally coherent video editing.
😎Report https://t.ly/tREX4
😎Paper https://arxiv.org/pdf/2308.14749.pdf
😎Project https://magic-edit.github.io/
😎Code github.com/magic-research/magic-edit
🌲 MagicEdit: Magic Video Editing 🌲
👉MagicEdit: explicit disentangling the learning of content, structure & motion for Hi-Fi and temporally coherent video editing.
😎Report https://t.ly/tREX4
😎Paper https://arxiv.org/pdf/2308.14749.pdf
😎Project https://magic-edit.github.io/
😎Code github.com/magic-research/magic-edit
🪶 ReST: Multi-Camera MOT 🪶
👉Novel reconfigurable two-steps graph model for multi-camera multi object video tracking (MC-MOT)
😎Review https://t.ly/3C5tb
😎Paper arxiv.org/pdf/2308.13229.pdf
😎Code github.com/chengche6230/ReST
💡 Relighting NeRF 💡
👉Neural implicit radiance representation for free viewpoint relighting of an object lit by a moving point light
😎Review https://t.ly/J-3_L
😎Project nrhints.github.io
😎Code github.com/iamNCJ/NRHints
😎Paper nrhints.github.io/pdfs/nrhints-sig23.pdf
🐨 Watch Your Steps: Editing by Text 🐨
👉The novel SOTA in image & scene (text) editing via denoising diffusion models
😎Review https://t.ly/fv9wn
😎Paper arxiv.org/pdf/2308.08947.pdf
😎Project ashmrz.github.io/WatchYourSteps
🥕 Scenimefy: I-2-I for anime 🥕
👉S-Lab unveils a novel semi-supervised I-2-I translation framework + HD dataset for anime
😎Review https://t.ly/IsdEG
😎Paper arxiv.org/pdf/2308.12968.pdf
😎Code https://github.com/Yuxinn-J/Scenimefy
😎Project https://yuxinn-j.github.io/projects/Scenimefy.html
🌆 NeO360: NeRF for Sparse Outdoor 🌆
👉#Toyota (+GIT) unveils NeO360: 360◦ outdoor scenes from a single or a few posed RGB images
😎Review https://t.ly/JDJZg
😎Paper arxiv.org/pdf/2308.12967.pdf
😎Project zubair-irshad.github.io/projects/neo360.html
🌵 POCO: 3D HPS using Confidence 🌵
👉 Novel framework for HPS regression: #3D human body + confidence in a single feed-forward pass
😎Review https://t.ly/cDePe
😎Paper arxiv.org/pdf/2308.12965.pdf
😎Project https://poco.is.tue.mpg.de
🦠 Instance-Level Semantics of Cells 🦠
👉TYC: novel dataset for understanding instance-level semantics & motions of cells in microstructures
😎Review https://t.ly/y-4VZ
😎Paper arxiv.org/pdf/2308.12116.pdf
😎Project christophreich1996.github.io/tyc_dataset/
😎Code github.com/ChristophReich1996/TYC-Dataset
😎Data tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3930
Hello everybody,
a lot of you asked me to open the comments to better enjoy the posts. I want to follow your suggestion, hope this new mood likes you.
🔥 NO SPAM
🔥 NO COMMERCIAL
🔥 NO UNRESPECTFUL MESSAGEs
🧡JUST AI & SCIENCE
⚠️ BAN AT THE FIRST VIOLATION ⚠️
🕹️ CoDeF: Video Content Deformation Fields 🕹️
👉Content deformation field is a new type of video representation for video-editing tasks
😎Review https://t.ly/PIVl-
😎Paper arxiv.org/pdf/2308.07926.pdf
😎Project https://qiuyu96.github.io/CoDeF
😎Code https://github.com/qiuyu96/CoDeF
⚡️Feature Matching at Light Speed⚡️
👉LightGlue is a lightweight feature matcher with high accuracy and blazing fast inference
😎Review https://t.ly/jkecX
😎Paper arxiv.org/pdf/2306.13643.pdf
😎Code github.com/cvg/LightGlue
🥎 SportsMOT + MixSort = Sports MOT 🥎
👉Nanjing just released a MOT dataset for sports scenes + the SOTA code/model for tracking (MixSort)
😎Review https://t.ly/NHUxL
😎Paper arxiv.org/pdf/2304.05170.pdf
😎Project deeperaction.github.io/datasets/sportsmot.html
😎Code github.com/MCG-NJU/MixSort
🛒 Digital Twins for AutoRetail Checkout 🛒
👉From #Nvidia a novel approach for using 3D assets for training 2D detection and tracking model in AutoRetail Checkout
😎Review https://t.ly/Ea7kt
😎Paper arxiv.org/pdf/2308.09708.pdf
😎Code github.com/yorkeyao/Automated-Retail-Checkout
🌈 Tracking by Persistent Dynamic View Synthesis 🌈
👉Novel simultaneous addressing of dynamic scene novel-view synthesis + 6-DOF tracking of all dense scene elements
😎Review https://t.ly/Bc535
😎Paper arxiv.org/pdf/2308.09713.pdf
😎Project dynamic3dgaussians.github.io
😎Code github.com/JonathonLuiten/Dynamic3DGaussians
🔥The code is out!🔥
😎 Code https://github.com/NVlabs/neuralangelo
🐘 Controllable Synthetic Data (extending Image-Net) 🐘
👉#META's PUG, a new generation of interactive environments for representation learning. Extending Image-Net!
😎Review https://t.ly/nCYs0
😎Paper arxiv.org/pdf/2308.03977.pdf
😎Project pug.metademolab.com
😎Code github.com/facebookresearch/PUG
👩🚀 HD Avatar via Text & Pose 👩🚀
👉 Generating expressive #3D avatars from nothing but text descriptions & pose guidance
😎Review https://t.ly/wrSMH
😎Paper arxiv.org/pdf/2308.03610.pdf
😎Project avatarverse3d.github.io
🎨 I-Paint: Interactive Neural Painting 🎨
👉 Novel AI-powered tool to help artists in completing their artworks
😎Review https://t.ly/ELUb0
😎Paper arxiv.org/pdf/2307.16441.pdf
😎Project helia95.github.io/inp-website
😎Supp helia95.github.io/inp-website/supp_mat.html
