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 17 144 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 701-o'rinni va Malayziya mintaqasida 2 225-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 144 obunachiga ega bo‘ldi.
25 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -186 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 23.94% 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 0 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 0 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 26 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.
17 144
Obunachilar
+324 soatlar
-367 kunlar
-18630 kunlar
Postlar arxiv
🌪️ TimeLapse++: Video Temporal Pyramid🌪️
👉Multi-scale lens to view the passage of time: far beyond a "classic" timelapse
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Inspired by "old-school" spatial pyramids
✅Video Spectrogram to go through pyramid
✅Months/years of data in a few seconds!
✅Multi-temporal freq., no aliasing
More: https://bit.ly/3TKnYPS
🥑 DALL·E: Outpainting via #NLP 🥑
👉Extending any original image, creating large-scale images in any aspect ratio
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Extending an image beyond its borders
✅Visual elements in same style of the input
✅Driving the image "story" in new directions
✅Shadows, reflections & textures w/ context
More: https://bit.ly/3eoH8uD
🪨Controllable #3D Adversarial Face🪨
👉#Meta (+CMU) on decoupling identity/expression + granular control over expressions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Supervised auto-enc. + GAN
✅UV texture maps + 3D faces
✅Control expression, saving ID
✅Code under X11 License
More: https://bit.ly/3AVE80q
🚗 Massive Dataset in Virtual Cities 🚗
👉Synthehicle: 7 hours of labeled material, 340 cams, 64 days, rain, dawn, & night scenes.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Multi-target multi-cam tracking
✅2D, 3D, segm. & depth annotations
✅Instance, semantic & panoptic segm.
✅340 clips, 64 scenes, 17 hrs, 4M BBs
More: https://bit.ly/3TArHiV
🧡 Avatarization in 90's. So Romantic 🧡
👉Making of the first #MortalKombat in early 90's
More: https://bit.ly/3wTSpJB
🍊StableFace: Talking Face Generation🍊
👉Analysis on motion jittering in 3D face generation (audio-in -> video-out)
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Motion jittering analysis for stability
✅Gaussian-based adaptive smoothing
✅Augmented erosions of neural renderer
✅Audio-fused generator for dependency
More: https://bit.ly/3Kt95gI
🎍#3D scene manipulation from 2D🎍
👉Reconstruct, decompose, manipulate & render 3D scenes in a single pipeline
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unique 3D, non-occupied space from 2D
✅Inverse query algorithm for shapes
✅First synthetic dataset for 3D editing
More: https://bit.ly/3RlYhTY
🔵 Deep Saliency: driving the attention 🔵
👉Google unveils a family of operators to "drive" human saliency
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Editing image to drive Saliency
✅Transforms to hide distractors
✅Warping operator for distractor
✅GAN-op for less-saliency altern.
More: https://bit.ly/3KoQQc2
🔥 #AIwithPapers: we are 4,000+! 🔥
💙💛Lot of people joined, and we talked about #StableDiffusion only twice! Can't believe it.💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
🔥IDOL (#CVPR2022 winner): code is out!🔥
👉IDOL for VIS: outperforming all online/offline methods, the new SOTA!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Online usually inferior by >10AP
✅Online based on contrast-learning
✅Discriminative++ instance embeddings
✅Full exploiting history for stability
More https://bit.ly/3dXCDXw
🦉PANDORA: Polarized Neural Decomposition🦉
👉CIL lab unveils PANDORA: polarimetric inverse rendering approach via INR
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Geometry, reflectance & illumination
✅normal, signed distance field, mesh
✅Diffuse-specular separation
✅Hi-fI incident illumination
More https://bit.ly/3CzGp3F
🍈 #StableDiffusion archive is out🍈
👉Lexica art is a Stable Diffusion prompt search engine. Real-time, countless #stablediffusion results for everyone. I had fun with the GOAT, #Maradona.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Maradona scoring against a capybara...
✅A poster of space jam with Maradona...
✅Painting of Maradona very detailed...
✅Painting of Maradona in heaven...
More: https://bit.ly/3PTXHLH
🌐RelPose: Probabilistic Relative Pose🌐
👉A novel method for core component in #SLAM / NeRF-powered apps.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Core component of SfM/SLAM
✅Pre-processing for neural (NeRF)
✅Energy-based over rotations
✅SOTA on both seen/unseen objects
More: https://bit.ly/3T60TXw
🍈DeepBillboards: old-school trick for #VR🍈
👉DeepBillboards models a 3D object implicitly using neural net on the user’s viewing direction
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅#Google Brain +Tsukuba + Tokyo
✅Rendering at higher res., improving #VR
✅NeRF into interactive VR with accuracy++
✅NeRF (or any others) directly in #Unity
More: https://bit.ly/3CsTQ5y
🥭Massive GTA-V human dataset🥭
👉GTA-Human: outperforming SOTA with a purely synthetic training.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅600+ gender, age, ethnicity & clothing
✅20,000+ clips, variety of human activities
✅6 categories of location, different BGs
✅Occlusions, lighting, and weather system
More: https://bit.ly/3wpZyRD
🔥 KeypointNeRF: code is out! 🔥
👉KeypointNeRF by #Meta: "NeRF"-avatars
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Generalizable NeRF for virtual avatar
✅Sparse 3D keypoints for SOTA avatar
✅Novel unseen subjects from 2/3 views
✅"iPhone" captures for #metaverse
More: https://bit.ly/3pyl17e
🔥🔥UPDATE🔥🔥
Code Released: https://github.com/facebookresearch/KeypointNeRF
🥑 CLIP-based Neural Style Transfer 🥑
👉From #Nvidia a novel method for transferring the style to a #3D object
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Texture style for 3D by CLIP-ResNet50
✅Nearest-neighbor feature matching loss
✅CLIP-based loss extraction of textures
✅NNFM for multiple style pics / control
✅No source code or models available 😒
More: https://bit.ly/3c32dK5
🍏NeuMan: Human NeRF in the wild🍏
👉#Apple opens a novel human pose/view from just a single in-the-wild video
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅No extra devices/annotations
✅Both Human (novel poses) + Scene
✅E2E SMPL optimization + error-corr.
✅Applications such as "telegathering"
More: https://bit.ly/3K4iTO6
🧰 FGT: flow-guided inpainting 🧰
👉#Microsoft (+USTC) unveils FGT: flow-guided ViT for video inpainting 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅OF into transformer for attention++
✅Flow completion net w/ local feats.
✅Dual perspective spatial MHSA
✅Local attention with global content
More: https://bit.ly/3pk5J5S
