Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
AI with Papers - Artificial Intelligence & Deep Learning
Kanalga Telegram’da o‘tish
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 17 142 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 723-o'rinni va Malayziya mintaqasida 2 241-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 142 obunachiga ega bo‘ldi.
23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -190 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 25.09% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.86% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 4 302 marta ko‘riladi; birinchi sutkada odatda 1 177 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 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 24 Iyun, 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.
17 142
Obunachilar
-224 soatlar
-367 kunlar
-19030 kunlar
Postlar arxiv
⚽ Dynamic NeRFs for Soccer ⚽
👉SoccerNeRF: first attempt of "cheap" NeRF applied to football for reconstructing soccer replays in space and time.
😎Review https://t.ly/Ywcvk
😎Paper arxiv.org/pdf/2309.06802.pdf
😎Project https://soccernerfs.isach.be/
😎Code github.com/iSach/SoccerNeRFs
🦊 MagiCapture: HD Multi-Concept Portrait 🦊
👉KAIST unveils MagiCapture: integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references
😎Review https://t.ly/c9rOo
😎Paper https://arxiv.org/pdf/2309.06895.pdf
🧄FreeMan: towards #3D Humans 🧄
👉FreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!
😎Review https://t.ly/ICxpA
😎Paper arxiv.org/pdf/2309.05073.pdf
😎Project wangjiongw.github.io/freeman
🔥🔥 #META's DINOv2 is now commercial! 🔥🔥
👉Universal features for image classification, instance retrieval, video understanding, depth & semantic segmentation. Now suitable for commercial.
😎Review https://t.ly/LNrGy
😎Paper arxiv.org/pdf/2304.07193.pdf
😎Code github.com/facebookresearch/dinov2
😎Demo https://dinov2.metademolab.com/
🪷 Diffusive Consistent Video Editing 🪷
👉 Weizmann Institute of Science unveils TokenFlow, a novel text-to-image diffusion model for text-driven video editing
😎Review https://t.ly/ru8km
😎Paper arxiv.org/pdf/2307.10373.pdf
😎Project diffusion-tokenflow.github.io
😎Code github.com/omerbt/TokenFlow
🍃 Tracking Anything with Decoupled VOS 🍃
👉A novel VOS approach that extends Segment Anything (SAM) to video for open-world video segmentation with no user input required
😎Review https://t.ly/xeobR
😎Paper arxiv.org/pdf/2309.03903.pdf
😎Project hkchengrex.com/Tracking-Anything-with-DEVA
😎Code github.com/hkchengrex/Tracking-Anything-with-DEVA
😎Colab https://colab.research.google.com/drive/1OsyNVoV_7ETD1zIE8UWxL3NXxu12m_YZ
♊️ Doppelgangers in Structures ♊️
👉A novel learning-based approach to visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions
😎Review https://t.ly/9yLot
😎Paper arxiv.org/pdf/2309.02420.pdf
😎Code github.com/RuojinCai/Doppelgangers
😎Project doppelgangers-3d.github.io/
⛺FACET: Fairness in Computer Vision⛺
👉#META AI opens a large, publicly available dataset for classification, detection & segmentation. Potential performance disparities & challenges across sensitive demographic attributes
😎Review https://t.ly/mKn-t
😎Paper arxiv.org/pdf/2309.00035.pdf
😎Dataset https://facet.metademolab.com/
🎍RoboTAP: Dense Tracking for Few-Shot Imitation🎍
👉RoboTAP is a novel dense tracking representation for robotic arm.
😎Review https://t.ly/MCO_V
😎Paper arxiv.org/pdf/2308.15975.pdf
😎Project https://robotap.github.io/
😎Code github.com/deepmind/tapnet
🐦 3D Pigeons Pose and Tracking 🐦
👉 3D-MuPPET: estimate and track 3D poses of pigeons with multiple-views
😎Review https://t.ly/jfAJJ
😎Paper arxiv.org/pdf/2308.15316.pdf
😎Code github.com/alexhang212/3D-MuPPET/
✂️ VideoCutLER: Super Simple UVIS ✂️
👉VideoCutLER is a simple unsupervised video instance segmentation (UVIS) method without relying on optical flows
😎Review https://t.ly/PBBjG
😎Paper arxiv.org/pdf/2308.14710.pdf
😎Project people.eecs.berkeley.edu/~xdwang/projects/CutLER
😎Code github.com/facebookresearch/CutLER/tree/main/videocutler
🌲 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
