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AI with Papers - Artificial Intelligence & Deep Learning

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

Ko'proq ko'rsatish

📈 Telegram kanali AI with Papers - Artificial Intelligence & Deep Learning analitikasi

AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 17 261 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 543-o'rinni va Malayziya mintaqasida 2 144-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 17 261 obunachiga ega bo‘ldi.

26 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 250 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 17.43% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 7.04% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 3 008 marta ko‘riladi; birinchi sutkada odatda 1 216 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 16 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent framework, object, dataset, tba, depth kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

17 261
Obunachilar
+124 soatlar
+97 kun
+25030 kun
Postlar arxiv
🌲 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

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🥕 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 inferenc
⚡️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
🛒 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

🐘 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