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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 17 136 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, 17 136 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.
17 136
Obunachilar
+324 soatlar
-367 kunlar
-18630 kunlar
Postlar arxiv
🐍 Implicitron: "democratizing" NeRF🐍
👉#META opens a novel framework for NeRF-world in #PyTorch3D #pytorch
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Implicit representations (NeRF) / Render
✅RaySampler/PointSampler & more
✅NeRF’s MLP, IDR’s FF, SRN, etc.
✅Renderers: MEAR, LSTMRenderer, etc.
More: https://bit.ly/3bPyJPJ
🔥Stable Diffusion on clips. INSANE🔥
👉The most advanced latent text-to-image DM. #RunwayML just announced is going to apply it on clips
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent DM on 512p from LAION-5B
✅Frozen CLIP ViT-L/14 text encoder
✅Lightweight, runs on a 10GB-GPU
✅Checkpoints only for research
More: https://bit.ly/3QfkRx3
🍨 Scaling Neural Indoor Scene 🍨
👉Neural scene rendering for indoor: scalable in both training/rendering
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Neural scene rendering for indoor
✅#3D into tiles with MLPs to scale up
✅Parallel training of tile-based MLPs
✅View-indep. components (via surf-MLP)
More: https://bit.ly/3bH94IX
🎰 Texturify: Neural Textures Generator 🎰
👉A step towards automated content creation. HQ textures directly on surface of 3D object
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅TUM + Max Planck + Apple 🍏
✅Realistic, HQ textures from 2D pics
✅3D shape geometry, no 3D supervision
✅3D-aware surface-based generation net
More: https://bit.ly/3BW7UUU
🪰 EasyMocap: Open Neural Mocap 🪰
👉EasyMocap: open-source marker-less mocap with novel view synthesis from RGB
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 (of last paper added):
✅Editable free-viewpoint video
✅Layered neural representation of humans
✅Multi-pax -> instances, weakly-supervised
✅HQ neural representation of the humans
✅Addressing camera error by human poses
More: https://bit.ly/3p6lUDO
🥇#NVIDIA wins SIGGRAPH's Best Paper🥇
👉Instant #NeRF awarded as a best paper at SIGGRAPH 2022!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Speed-up of several orders of magnitude
✅HQ neural primitives in a matter of secs
✅Render in tens of milliseconds at 1080p
✅Source code and resources available!
More: https://bit.ly/3Qt8c9D
🧊EPro-PnP: Persp-n-Points Detection🧊
👉EPro-PnP: probabilistic PnP layer for general e2e pose estimation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Probabilistic PnP for general e2e pose
✅Top-tier in 6DoF by inserting into CDPN
✅Deformable accurate detection
✅2D-3D corresp. learned from scratch
More: https://bit.ly/3BNPXYr
🎹🎹 Learning Piano in #AR 🎹🎹
👉PianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Sheet Insight to learn sight-read
✅MIDI keyboard connectivity
✅Air piano for no physical pianos
✅Multiplayer Music Instruction
✅PianoVision Music Hall in #VR
More: https://bit.ly/3zYvwGX
🍑 World-Object Detection via ViT 🍑
👉Google unveils OWL-ViT: open-vocabulary detector based on ViTs 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ViTs for Open-World Localization
✅Img-level to open-vocabulary detection
✅SOTA one-shot (img.cond.) detection
More: https://bit.ly/3Sy3jOj
🔥PCVOS: clip-wise mask VOS🔥
👉PCVOS: new semi-supervised video object segmentation method
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Reformulating semi-supervised VOS
✅Novel per-clip inference perspective
✅Clip-wise operation on intra-clip
✅PCVOS: model for per-clip inference
✅New SOTA on multiple benchmarks
More: https://bit.ly/3vJtmbz
☀️LocoProp: Neural Layers Composition☀️
👉Google AI unveils LocoProp: novel neural paradigm for modular composition of layers.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Backprop++ via Local Loss Optimization
✅Layer-based w-reg, target output, loss
✅Multiple local update via first-order opt.
✅Superior performance and efficiency
More: https://bit.ly/3Q40YJn
🔥🔥MultiNeRF: three NeRFs are out!🔥🔥
👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Paper_1: Mip-NeRF 360
✅Paper_2: Ref-NeRF
✅Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
🔥 MinVIS, a new SOTA is out 🔥
👉#Nvidia miniVIS: no video-based architectures nor training procedures🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video architecture/train not required
✅MinVIS outperforms the previous SOTA
✅Occluded VIS (OVIS): >10% improvement
✅1% of labeled frames >> fully-supervised
More: https://bit.ly/3pcYzk1
🚀 #VR by NASA - 1985 🚀
👉Q: is #VR the technology that developed least in the last 40 years? 🤔
Let's talk: https://bit.ly/3JxDZ7i
👩🦰 Real-Time Neural Hair 👩🦰
👉Accurate hair geometry & appearance from multi-pics
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Bonn, CMU and Reality Labs
✅Photorealistic Real-Time render
✅HQ strand geometry/appearance
✅Novel scalp texture description
✅Intuitive manipulation of 3D hair
More: https://bit.ly/3vBiH2G
🧣NeRF for Outdoor Scene Relighting🧣
👉NeRF-OSR: the first neural radiance fields approach for outdoor scene relighting
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF-method for outdoor relighting
✅Simultaneous illumination/viewpoint
✅Control over shading, shadow, albedo
✅Self-Supervised training from outdoor
✅Dataset: 3240 viewpoints, 110+ times
More: https://bit.ly/3vBiH2G
🔥🔥UPDATE🔥🔥
Code Released: https://github.com/andreas128/RePaint
🔥 MobileNeRF is out -> Pure Fire! 🔥
👉MobileNeRF is out: the mobile evolution of NeRF via textured polygons.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Same quality, 10x faster than SNeRG
✅Memory-- by storing surface textures
✅Integrated GPUs: less memory/power
✅Suitable for browser & viewer is HTML
More: https://bit.ly/3PUKPWy
🔥AND/OR: Composable Diffusion Models🔥
👉Novel neural compositional generation via Composable Diffusion Models
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DM as energy-based models
✅Connecting diffusion models
✅Conjunction & negation, on top of DM
✅Zero-shot combinatorial generalization
More: https://bit.ly/3PYv1Cs
