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لا تقع ضحية للمخادعين! تيليمتريو يكتشف ويُميّز هذه القنوات 👉 إذا كنت تريد رؤية العلامة، اشترك

AI with Papers - Artificial Intelligence & Deep Learning

AI with Papers - Artificial Intelligence & Deep Learning

الذهاب إلى القناة على Telegram

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

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام AI with Papers - Artificial Intelligence & Deep Learning

تُعد قناة AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 17 261 مشتركاً، محتلاً المرتبة 7 532 في فئة التكنولوجيات والتطبيقات والمرتبة 2 128 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 261 مشتركاً.

بحسب آخر البيانات بتاريخ 29 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 219، وفي آخر 24 ساعة بمقدار 0، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 16.46‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 7.09‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 2 842 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 223 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 17.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل framework, object, dataset, tba, depth.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 30 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

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أرشيف المشاركات
🦉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

🥑 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

🐍 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