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
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 17 259 مشتركاً، محتلاً المرتبة 7 509 في فئة التكنولوجيات والتطبيقات والمرتبة 2 130 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 259 مشتركاً.
بحسب آخر البيانات بتاريخ 25 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 261، وفي آخر 24 ساعة بمقدار 2، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 16.91%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 7.04% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 918 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 216 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 16.
- 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 26 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
17 259
المشتركون
+224 ساعات
+17 أيام
+26130 أيام
أرشيف المشاركات
🦖T-Rex: Counting by Visual Prompting🦖
👉T-Rex: a novel interactive object counting model to detect and count any objects. Impressive results!
👉Review https://t.ly/4SfFX
👉Project https://lnkd.in/dVtEndHv
👉Paper https://lnkd.in/dBGQsbdP
👉Code (not announced, but an empty repo exists): https://lnkd.in/dnZnGRUn
🧿 Model-aware 3D Eye Gaze 🧿
👉 Novel hybrid approach that outputs 3D eye model, semantic segmentation, cam-intrinsic & pose. Only 2D eye semantic segmentation masks and fewer 3D gaze labels for supervision.
👉Review https://t.ly/AdKRf
👉Paper https://lnkd.in/dWb9GHPh
👉Code https://lnkd.in/dfAWFVky
🔳 SOTA Semantic Boundary 🔳
👉Mobile-Seed, a lightweight, dual-task framework tailored for simultaneous semantic segmentation and boundary detection.
👉Review https://t.ly/GsArZ
👉Project whu-usi3dv.github.io/Mobile-Seed/
👉Paper arxiv.org/pdf/2311.12651.pdf
👉Code github.com/WHU-USI3DV/Mobile-Seed
🍿 Segmenting anything in 3D 🍿
👉 OmniSeg3D: omniversal segmentation method aims for segmenting anything in 3D all at once.
👉Review https://t.ly/Q0jrK
👉Paper https://lnkd.in/d9qpxXY9
👉Code (soon)
🌦️ 100+ GPU weather training 🌦️
👉#NVIDIA just released Makani: massively parallel training of weather and climate prediction models on 100+ GPUs and to enable the development of the next generation of weather and climate models.
👉 Discussion https://lnkd.in/dMgakzWm
👉 Project & Code https://lnkd.in/d4NFZ5xi
🐓 Emu: image edit / video gen. 🐓
👉#Meta the new SOTA in text-to-video generation and instruction-based image editing.
👉 Review https://t.ly/PMTBc
👉 Paper (image edit): https://lnkd.in/eVadH-QS
👉 Project https://lnkd.in/eG8eWUJY
👉 Paper (video gen): https://lnkd.in/eVadH-QS
👉 Project https://lnkd.in/eu6Zu6gp
💥🚗 CrashCar101: Generative Damaged Cars💥🚗
👉 CrashCar101: procedural generation pipeline that damages 3D car models to obtain synthetic damaged cars paired with pixel-accurate annotations
👉 Review https://t.ly/pITHm
👉 Paper https://lnkd.in/dzp6q3T5
👉 Project https://lnkd.in/daRXg73N
🔥Florence-2: unified Computer Vision🔥
👉#Microsoft announces Florence-2: novel foundation model with unified, prompt-based, representation for a large variety of #computervision & vision-language task. One backbone -> multiple tasks!
👉Review https://t.ly/pOins
👉Paper arxiv.org/pdf/2311.06242.pdf
👉Project www.microsoft.com/en-us/research/project/projectflorence/
🪐 Spacecraft Pose Estimation 🪐
👉SnT (Luxembourg) unveils the most advanced event-based dataset for Spacecrafts: Unreal Engine + data from ICNS simulator + Real images + Real event data acquired in lab
😎Review https://t.ly/m8JPB
😎Paper https://lnkd.in/d_edvc3n
😎Project https://lnkd.in/dPp375aY
🫀 Neural Segmentation of Human 🫀
👉TotalSegmentator_v2: segmenting 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) in CT. Now suitable in 3D Slicer, open source platform for image visualization.
😎Review https://t.ly/yHMm1
😎Code https://lnkd.in/dvgrbsCE
😎Paper https://lnkd.in/dkwHuuzU
🔥 Hu.ma.ne #AI Pin is out! 🔥
👉Hu.ma.ne just launched #AI Pin: the new standalone AI-powered screenless device. Running on the GPT-4 LLMs, suitable for real-time translation. #AI-powered camera and laser projector
😎 More https://t.ly/CXz95
🐪 30x Faster Neural Large Scenes 🐪
👉 NeuRas: realistic real-time novel-view synthesis of VERY large scenes (>10000 m2 ). 30× faster rendering than previous SOTA w/ comparable or better realism
😎Review https://t.ly/ELJSE
😎Paper https://arxiv.org/pdf/2311.05607.pdf
😎Project https://waabi.ai/NeuRas/
🛋️ 3DiffTection: new SOTA 3D detection 🛋️
👉#Nvidia unveils 3DiffTection, the new SOTA for 3D object detection from single images. A powerful 3D detector powered by diffusion model
😎Review https://t.ly/PciXY
😎Project research.nvidia.com/labs/toronto-ai/3difftection
😎Paper https://arxiv.org/pdf/2311.04391.pdf
😎Code https://github.com/nv-tlabs/3DiffTection
🥻Sewformer: Towards Virtual Cloth🥻
👉SEA AI Lab unveils a novel #AI to recovery the garment sewing patterns from daily photos for #AR / #VR worlds
😎Review https://t.ly/MwpAV
😎Project https://sewformer.github.io/
😎Paper https://arxiv.org/pdf/2311.04218.pdf
😎Code https://github.com/sail-sg/sewformer
🔥 Does GPT-4 Pass the Turing Test? 🔥
👉No. I mean...not yet. Read this Paper from UC San Diego👇
😎Review https://t.ly/o8HgM
😎 Paper https://arxiv.org/pdf/2310.20216.pdf
🚛 OYSTER: unsupervised detection w/ LIDAR 🚛
👉Waabi unveils OYSTER: a novel unsupervised object detection from LiDAR point clouds.
😎Review https://t.ly/EMi58
😎Project https://waabi.ai/oyster/
😎Paper arxiv.org/pdf/2311.02007.pdf
👣 Foot via Synthetic Data 👣
👉 50,000 synthetic/photorealistic foot images + a novel SOTA library for foot
😎Review https://t.ly/TVanP
😎Paper https://arxiv.org/pdf/2310.18279.pdf
😎Project https://ollieboyne.github.io/FOUND
😎Code https://github.com/OllieBoyne/FOUND
🍄 Video Understanding with GPT-4V(ision) 🍄
👉 #Microsoft unveils MM-Vid, the most advanced video understanding framework (w/ #chatgpt4). Impressive results on long-form videos and intricate tasks such as audio description & multimodal high-level comprehension
😎Review https://t.ly/RISMm
😎Paper arxiv.org/pdf/2310.19773.pdf
😎Project https://multimodal-vid.github.io
✌️ Relighted 3D Interacting Hands 🤞
👉#META unveils Re:InterHand: a large dataset of relighted 3D interacting hands
😎Review https://t.ly/I1dQk
😎Paper arxiv.org/pdf/2310.17768.pdf
😎Project mks0601.github.io/ReInterHand
😎Data github.com/mks0601/ReInterHand
🧂 SOTA RGB-D Video Salient Object 🧂
👉 DCTNet+ (model) and RDVS(dataset) for a new SOTA in Video Saliency Object Detection
😎Review https://t.ly/DapLV
😎Code github.com/kerenfu/RDVS
😎Paper arxiv.org/pdf/2310.15482.pdf
