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 17 262 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 510-o'rinni va Malayziya mintaqasida 2 073-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 262 obunachiga ega bo‘ldi.
08 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 20 ga, so‘nggi 24 soatda esa -6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 17.76% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.88% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 064 marta ko‘riladi; birinchi sutkada odatda 1 187 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 12 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 09 Noyabr, 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.
17 262
Obunachilar
-624 soatlar
-127 kun
+2030 kun
Postlar arxiv
🔥 Semantic VISOR dataset is out! 🔥
👉Segmenting hands / active objects in egocentric video (millions masks)
😎Review https://bit.ly/3LOBLBv
😎Project epic-kitchens.github.io/VISOR/
😎Paper arxiv.org/pdf/2209.13064.pdf
🔥Diffusion Model of Neural Checkpoints🔥
👉Conditional diffusion model on Millions of checkpoints of a given task/architecture 🤯
😎Review https://bit.ly/3SBR4Qb
😎Project www.wpeebles.com/Gpt
😎Code github.com/wpeebles/G.pt
😎Paper arxiv.org/pdf/2209.12892.pdf
🔥🔥 IDE-3D: source code is out! 🔥🔥
👉Novel, photorealistic, 3D-aware facial generator: source code just released!
😎Review https://bit.ly/3BNrO2C
😎Project mrtornado24.github.io/IDE-3D/
😎Code github.com/MrTornado24/IDE-3D
😎Paper arxiv.org/pdf/2205.15517.pdf
👛 #Nvidia GET3D: #3D generative #AI 👛
👉AI-based Textured 3D meshes with complex topology, rich geometry & hi-fi textures
😎Review https://bit.ly/3SgnT5h
😎Code github.com/nv-tlabs/GET3D
😎Project nv-tlabs.github.io/GET3D/
😎Paper nv-tlabs.github.io/GET3D/assets/paper.pdf
💹 Image Synthesis @160+ FPS! 💹
👉Super-fast, 3D-Aware Image Synthesis with Sparse Voxels -> up to 167 FPS!
😎Review https://bit.ly/3r3ZNij
😎Paper arxiv.org/pdf/2206.07695.pdf
😎Project katjaschwarz.github.io/voxgraf
🦠 Motion Transformer for #selfdriving 🦠
👉The 1st place solution for 2022 #waymo "motion prediction" challenge
😎Review https://bit.ly/3f8G4LD
😎Paper arxiv.org/pdf/2209.10033.pdf
😎Code github.com/sshaoshuai/MTR
🔥#Google just announced "TensorStore"🔥
👉Novel open-source C++ / #Python library for storage/manipulation of high-dim data
😎Review https://bit.ly/3DLwbha
😎Project https://bit.ly/3C4T2TR
😎Code github.com/google/tensorstore
🍜 SURF-GAN: NeRF - >StyleGAN 🍜
👉 Editable portraits by injecting the NeRF's prior into StyleGAN
😎Review https://bit.ly/3SohEw3
😎Project jgkwak95.github.io/surfgan
😎Paper arxiv.org/pdf/2207.10257.pdf
😎Code github.com/jgkwak95/SURF-GAN
🥶 Lumos by #Nvidia: Relighting Portrait 🥶
👉The new SOTA in relighting without requiring a light stage
😎Review https://bit.ly/3dCH9ej
😎Project deepimagination.cc/Lumos
😎Paper arxiv.org/pdf/2209.10510.pdf
😎Demo http://imaginaire.cc/Lumos/
🚜 NeRF-Factory: a NeRF collection 🚜
👉PyTorch-reimplemented NeRF library with 7 popular models/implementations & 7 datasets
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF: Project | Paper | Code
✅NeRF++: Paper | Code
✅DVGO: Project | Paper v1/v2 | Code
✅Plenoxels: Project | Paper | Code
✅Mip-NeRF: Project | Paper | Code
✅Mip-NeRF360: Project | Paper | Code
✅Ref-NeRF: Project | Paper | Code
More: https://bit.ly/3qUgmgC
🦪StereoVoxelNet: RT Obstacles Detection🦪
👉Novel deep neural approach to detect occupancy from stereo images directly
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Occupancy voxels via deep learning
✅RT on Jetson-TX2 (-98% CPU of SOTA)
✅Optimization via octrees / sparse conv.
✅Real-world stereo in/outdoor dataset
More: https://bit.ly/3BylAn3
🍈SegNeXt: new SOTA in Semantic Seg.🍈
👉SOTA (by large margin) on ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel tailored network architecture
✅Spatial attention via multi-scale feats
✅Encoder + conv. better than transformers
✅SOTA on several datasets (ADE20K, etc.)
More: https://bit.ly/3UrZhrH
🍋YOLOPv2: Better Driving Perception🍋
👉YOLOPv2: simultaneous object, road segmentation & lane detection
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅E2E perception net with better backbone
✅Efficient ELAN for reasonable memory
✅Stability for adapting to scenarios
✅SOTA on BDD100K, +50% faster!
✅Source code under MIT license
More: https://bit.ly/3LvYGBh
🐸 CHARL-E: Stable Diffusion in 1 click 🐸
👉CHARL-E packages Stable Diffusion into a simple app.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅No setup, dependencies, or internet
✅Images with 1-click on #macbook
✅Suitable only for M1/M2 processor
✅Source code under MIT license
More: https://bit.ly/3xv2z3G
🍐PeRFception: Largest IR Dataset🍐
👉#Nvidia, a new frontier in data collection via Plenoxels: same info, -96.4% in size.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅POSTECH + NVIDIA + Caltech = 🤯
✅Size: -96.4% from original dataset!
✅2D/3D image/object class/semantic
✅Ready-to-use pipeline for implicit dataset
More: https://bit.ly/3eW9hJA
🟨 Lang<->Pics in 100+ Languages 🟨
👉#Google PaLI: unified lang-image #AI to perform tasks in 109 languages 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅PaLI: Pathways Lang & Image model
✅Answering, captioning, reasoning, etc
✅From Eng. to 109 lang. understanding
✅The new SOTA on several datasets
More: https://bit.ly/3QMslHC
🈯SAMURAI: in-the-wild Shape/Material🈯
👉#Google SAMURAI: shape, BRDF, per-image pose & illumination. Relightable #3D assets for #AR/#VR.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Parametrization for varying distances
✅Camera multiplex optimization
✅Posterior scaling of input images
✅Explicit meshes extraction with BRDF
✅Code/data soon available ->#NeurIPS
More: https://bit.ly/3BKWgf3
🉐#AI finds where IG photos are taken🉐
👉Brilliant work of Depoorter, Belgium artist that handles #privacy, #AI & #socialmedia
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Recorded open cameras for weeks
✅Scraped all #Instagram photos
✅Matching Instagram vs. footage
More: https://bit.ly/3eL5dfc
🔥🔥 UPDATE 🔥🔥
Code Released: https://github.com/sczhou/CodeFormer
🔥 A Survey on Diffusion Models 🔥
👉A comprehensive review of denoising diffusion models in #computervision 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Overview on diffusion models
✅Hot trend for the generative AI
✅A multi-perspective categorization
✅Current limitations / new directions
More: https://bit.ly/3RYG5zP
