متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
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 17 173 مشتركاً، محتلاً المرتبة 7 725 في فئة التكنولوجيات والتطبيقات والمرتبة 2 238 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 173 مشتركاً.
بحسب آخر البيانات بتاريخ 19 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -177، وفي آخر 24 ساعة بمقدار -9، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 21.83%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً N/A% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 3 749 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 26.
- 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 20 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
17 173
المشتركون
-924 ساعات
-397 أيام
-17730 أيام
أرشيف المشاركات
💜MoRo: Human Motion Recovery💜
👉Masked modeling for human motion Recovery under Occlusions. Given a monocular video captured from a static camera, MoRo (by ETHZ & #Meta) robustly reconstructs accurate/physically plausible human motion, even under challenging occlusions. Repo released💙
👉Review https://t.ly/kK_je
👉Paper arxiv.org/pdf/2601.16079
👉Project mikeqzy.github.io/MoRo/
👉Repo github.com/mikeqzy/MoRo
🦧VideoMaMa: Mask-Guided Matting🦧
👉VideoMaMa is novel a diffusion-based model that converts binary segmentation masks into continuous alpha mattes. Repo, Dataset & Demo💙
👉Review https://t.ly/l_0f8
👉Paper arxiv.org/pdf/2601.14255
👉Project cvlab-kaist.github.io/VideoMaMa
👉Repo github.com/cvlab-kaist/VideoMaMa
👉Demo huggingface.co/spaces/SammyLim/VideoMaMa
💊Foundation Medical SAM3 💊
👉Medical SAM3: foundation model for universal prompt-driven medical image segmentation, by fully fine-tuning SAM3 on large-scale, heterogeneous 2D/3D medical imaging datasets with paired segmentation masks-text prompts. Repo & Demo announced💙
👉Review https://t.ly/C6jcy
👉Paper https://arxiv.org/pdf/2601.10880
👉Project chongcongjiang.github.io/MedicalSAM3/#
👉Repo github.com/AIM-Research-Lab/Medical-SAM3
💚 #META 3D Casual Captures 💚
👉#META unveils ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Impressive results. Repo under CC BY-NC 4.0💙
👉Review https://t.ly/j08sJ
👉Paper arxiv.org/pdf/2601.11514
👉Project facebookresearch.github.io/ShapeR/
👉Repo github.com/facebookresearch/ShapeR
👹SOTA Part-level Generator👹
👉A novel a text-to-motion model that learns to compose complex motions through hierarchical conditioning on part-, action- & sequence-level text, enabling fine-grained control over body parts & timing. Code, models & Dataset to be released💙
👉Review https://t.ly/leB_R
👉Paper arxiv.org/pdf/2601.10909
👉Project coral79.github.io/frankenmotion/
👉Repo github.com/Coral79/FrankenMotion-Code
💢3D Human Gen-Seg💢
👉CoMoVi takes an input image with a text description and generates 3D human motion & video sequence synchronously within a single diffusion denoising loop. Repo & Dataset releasing💙
👉Review https://t.ly/khSkm
👉Paper arxiv.org/pdf/2601.10632
👉Project igl-hkust.github.io/CoMoVi/
👉Repo github.com/IGL-HKUST/CoMoVi
👉Data huggingface.co/datasets/AfterJourney/CoMoVi-Dataset
💜Interactive Humanoid Generation💜
👉FlowAct-R1 by ByteDance is a novel framework that enables lifelike, responsive, and high-fidelity humanoid video generation for seamless real-time interaction. No code but impressive results (see video with audio) 💙
👉Review https://t.ly/aQhol
👉Paper arxiv.org/pdf/2601.10103
👉Project grisoon.github.io/FlowAct-R1/
🍿100M Video Action Dataset🍿
👉Action100M by META is a large-scale dataset w/ 1.2M instructional videos (14.6 years of duration), yielding O(100M) temporally localized segments with open-vocabulary action supervision and rich captions. Repo under FAIR NC Research License💙
👉Review https://t.ly/w5KXe
👉Paper https://arxiv.org/pdf/2601.10592
👉Repo https://github.com/facebookresearch/Action100M
🎇 Multi-target SAM3 🎇
👉SAM3-DMS is a novel training-free decoupled strategy that utilizes fine-grained memory selection on individual objects. Robust identity preservation and tracking stability. Repo under SAM License💙
👉Review https://t.ly/jJOAr
👉Paper https://arxiv.org/pdf/2601.09699
👉Repo https://github.com/FudanCVL/SAM3-DMS
💚 Segment Anything w/ Geometry💚
👉3AM (NYCU + #Nvidia) offers cross-view correspondence even under large viewpoint changes, cluttered scenes, and variations in capture conditions, enabling robust object tracking from both videos & casual multi-view images. Repo (coming) & Demo available💙
👉Review https://t.ly/olZwE
👉Paper https://arxiv.org/pdf/2601.08831
👉Project https://jayisaking.github.io/3AM-Page/
👉Repo https://github.com/jayisaking
👉Demo https://huggingface.co/spaces/nycu-cplab/3AM
👉Games Workshop (Warhammer) is banning the use of AI in creative and design processes to protect IP and human creativity. A decision that goes against the current hype of widespread AI adoption.
And what about your organization? I need your help👇
Vote: https://www.linkedin.com/posts/visionarynet_ai-activity-7417106327019196417-TpGL
In 2026, who should we keep an eye on?
Vote: https://www.linkedin.com/posts/visionarynet_ai-deeplearning-aiwithpapers-activity-7416886610795077632-qQeP/
🫛Active Object Reconstruction🫛
👉ObjSplat (Beijing) autonomously plans viewpoints and progressively reconstructs an unknown object into a Hi-Fi Gaussian model and water-tight mesh, enabling direct use in physics simulations. Repo announced💙
👉Review https://t.ly/au6HE
👉Paper arxiv.org/pdf/2601.06997
👉Project li-yuetao.github.io/ObjSplat-page/
👉Repo https://github.com/Li-Yuetao/ObjSplat
🔥Orient Anything V2 is out🔥
👉Orient Anything V2 is a foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Repo under CC-BY-4.0💙
👉Review https://t.ly/Ht7Xd
👉Paper arxiv.org/pdf/2601.05573
👉Project orient-anythingv2.github.io/
👉Repo github.com/SpatialVision/Orient-Anything-V2
🔥 New #AI Startups in 2026? 🔥
In 2026, which area would you focus on?
🤖Agents → workflows, copilots, etc.
🏭Vertical AI → Pharma, Automotive, Energy ...
🧠Infrastructure → MLOps, Security, Cost Control ...
🎨AI for Creators/Media → Video, avatars, contents ...
Please, help me understanding what's next with this poll on LinkedIn :)
https://www.linkedin.com/posts/visionarynet_ai-ai-deeplearning-activity-7415377341779996672-sQO1
LUV U \m/
🌍Label Any Object in 3D 🌍
👉LabelAny3D: novel analysis-by-synthesis framework that reconstructs holistic 3D scenes from 2D to efficiently produce HQ 3D BBs annotations. Repo under CC-BY-4.0 license💙
👉Review https://t.ly/bO93j
👉Paper https://lnkd.in/dYb97zWG
👉Project https://lnkd.in/dJ9UKERb
👉Repo https://lnkd.in/d9SxtmiA
🦙 Depth as Neural Implicit 🦙
👉InfiniDepth represents depth as neural implicit fields, "infinite" (i.e.16K) resolution and geometrical details. Repo under Apache 2.0💙
👉Review https://t.ly/4we5t
👉Paper https://lnkd.in/dpiHQExj
👉Project https://lnkd.in/dy3JxKye
👉Repo https://lnkd.in/dAXbnK5z
⭐ TOP 5 Papers you loved in 2025 ⭐
👉 In 2025 novel architectures have redefined efficiency and accuracy, and almost every day brought a new SOTA in image understanding, tracking, and #GenAI. It’s been an inspiring ride, and 2026 it will be even wilder. This community (LinkedIn + Telegram) is now around 80,000+ people.
𝐏𝐚𝐩𝐞𝐫𝐬 (𝐛𝐲 𝐲𝐨𝐮𝐫 𝐩𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞):
⭐3D LLM Understanding https://t.ly/ejr1s
⭐DynOMo is out https://t.ly/t5pCf
⭐Tracking Transformations https://t.ly/NPyW4
⭐YOLOv12 (new SOTA) https://t.ly/jj1oR
⭐Gaussian Surface Tracking https://t.ly/udpMq
Thank you all💙
