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

📈 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 17 119 مشتركاً، محتلاً المرتبة 7 678 في فئة التكنولوجيات والتطبيقات والمرتبة 2 208 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 119 مشتركاً.

بحسب آخر البيانات بتاريخ 29 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -162، وفي آخر 24 ساعة بمقدار -10، مع بقاء الوصول العام مرتفعاً.

حالة التحقق: غير موثّقة
معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 17.42‎%. 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 2 983 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 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 30 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

17 119

المشتركون

-1024 ساعات

-277 أيام

-16230 أيام

2 983

عرض المشاهدات

لا توجد بيانات24 ساعات

لا توجد بيانات48 ساعات

17.42%

معدل المشاركة

لا توجد بيانات

المشاركات في اليوم

Ads index

beta

أرشيف المشاركات

17 119

🍓Surface Light Tokenizer🍓 👉Apple unveils LITO a novel latent flow matching model enables HQ image-to-3D. Latent representation that encodes a surface light field into a compact set of latent vectors. Impressive results but no code🥲 👉Review https://t.ly/xcWNe 👉Paper https://lnkd.in/dYHwY4YX 👉Project https://lnkd.in/dtJT8bXy

17 119

🔥Holistic 3D Spatial Intelligence🔥 👉Holi-Spatial is the first fully automated pipeline capable of converting raw video streams into holistic 3D spatial annotations without human intervention. Code/Data announced💙 👉Review https://t.ly/PDpr9 👉Paper https://lnkd.in/dTbMuZCm 👉Project https://lnkd.in/d66CYB4q 👉Repo https://lnkd.in/dAGzShXj

17 119

📊Real-Time Scene Graph📊 👉REACT++ by Umea University is the new state-of-the-art model for real-time SGG: 20% faster with a gain of 10% in relation prediction accuracy on average. Code under MIT💙 👉Review https://t.ly/c12VX 👉Paper https://arxiv.org/pdf/2603.06386 👉Repo https://github.com/Maelic/SGG-Benchmark

17 119

🎪SOTA Arbitrary Tracking🎪 👉TAPFormer is the novel SOTA transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-freq point tracking. Repo & Dataset under MIT💙 👉Review https://t.ly/-q4wm 👉Paper https://arxiv.org/pdf/2603.04989 👉Project http://tapformer.github.io/ 👉Repo https://github.com/ljx1002/TAPFormer

17 119

🍧Monocular 3D Clothed Human🍧 👉MultiGO++ is a novel framework for monocular 3D clothed human reconstruction via geometry-texture collaboration. New SOTA but no code announced🥲 👉Review https://t.ly/YKY44 👉Paper arxiv.org/pdf/2603.04993 👉Project 3dagentworld.github.io/multigo++

17 119

Could be useful for you seeing a few (verified) job posting about AI in this channel?

Anonymous voting

17 119

🍙Any Resolution, Any Geometry🍙 👉Ultra Resolution Geometry Transformer (URGT) for arbitrary resolutions (e.g. 4K, 6K, 8K) depth–normal estimation. New SOTA. Repo under MIT💙 👉Review https://t.ly/HXg1n 👉Paper arxiv.org/pdf/2603.03026 👉Project dreamaker-mrc.github.io/Any-Resolution-Any-Geometry/ 👉Repo github.com/Dreamaker-MrC/Any-Resolution-Any-Geometry

17 119

🐪DuoMo: Dual Motion Diffusion🐪 👉DuoMo by #Meta is a novel generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Code announced💙 👉Review https://t.ly/dnA3K 👉Paper arxiv.org/pdf/2603.03265 👉Project yufu-wang.github.io/duomo/ 👉Repo TBA

17 119

🪿All Point Clouds-One Encoder🪿 👉Utonia is a step toward one-from-all and one-for-all point cloud encoder. It pretrains a single encoder on diverse point cloud data and reuses it as a reliable backbone for downstream tasks. Code under Apache 2.0💙 👉Review https://t.ly/yqSyZ 👉Paper https://arxiv.org/pdf/2603.03283 👉Project https://pointcept.github.io/Utonia/ 👉Repo https://github.com/Pointcept/Utonia

17 119

🍓Fully Offline Mobile-VTON🍓 👉A novel, hq, privacy-preserving framework that enables fully offline virtual try-on on commodity mobile devices using only a single user image and a garment image. Repo announced, to be released💙 👉Review https://t.ly/dsrIn 👉Paper arxiv.org/pdf/2603.00947 👉Project zhenchenwan.github.io/Mobile-VTON/ 👉Repo https://github.com/tmllab/2026_CVPR_Mobile-VTON

17 119

🦜Geometry-Aware 4D Head🦜 👉 GeoDiff4D is a novel framework that reconstructs animatable 4D head avatars from a single portrait image through geometry-aware diffusion. Code announced💙 👉Review https://t.ly/J9L-t 👉Paper https://lnkd.in/ddpv-78g 👉Project https://lnkd.in/d-vhukyj 👉Repo https://lnkd.in/dzd6mnFv

17 119

🧱Solaris: generative #Minecraft🧱 👉NYU unveils Solaris, multiplayer video world model in Minecraft, which generates consistent first-person observations for two players simultaneously. Impressive work. Repo & Dataset💙 👉Review https://t.ly/VrcrT 👉Paper https://arxiv.org/pdf/2602.22208 👉Project https://solaris-wm.github.io/ 👉Repo https://github.com/solaris-wm/

17 119

🫸 World-Grounded Hand-Object🫸 👉Given SLAMed egocentric videos, unlike existing methods that predict either hands or object poses separately, WHOLE jointly reconstructs coherent hand and object motion in the world space by guiding a generative motion prior. Code announced💙 👉Review https://t.ly/c5w8h 👉Paper https://arxiv.org/pdf/2602.22209 👉Project https://judyye.github.io/whole-www/ 👉Repo TBA

17 119

🔥New SOTA Planar Tracking🔥 👉WOFTSAM by the Visual Recognition Group (CTU) is a novel planar tracker that combine robust long-term segmentation by SAM2 with 8 degrees-of-freedom homography pose estimation. Repo under BY-NC-SA 4.0💙 👉Review https://t.ly/VUOe5 👉Paper https://lnkd.in/dZfc_DhQ 👉Repo https://lnkd.in/dAcneJGn

17 119

🚤Video Neural Compression🚤 👉TeCoNeRV by UMD is a framework for adapting INR hypernetworks to compress videos efficiently at higher resolutions. Impressive results: +5.35dB PSNR @720p on UVG, -36% bitrates & 1.5-3× faster encoding. Code announced💙 👉Review https://t.ly/0AtCK 👉Paper arxiv.org/pdf/2602.16711 👉Project namithap10.github.io/teconerv/ 👉Repo github.com/namithap10/TeCoNeRV/

17 119

🐙Dex4D: Task-Agnostic Track🐙 👉Dex4D by CMU is a novel approach for unseen objects and poses, scene layouts, backgrounds, & task trajectories. Code under Apache 2.0💙 👉Review https://t.ly/ZGx9T 👉Paper arxiv.org/pdf/2602.15828 👉Project dex4d.github.io/ 👉Sim github.com/Dex4D/Dex4D-Simulation 👉Vision github.com/Dex4D/Dex4D-Vision 👉HW https://github.com/Dex4D/Dex4D-Hardware

17 119

📲 Efficient VLMs 📲 👉CoPE-VideoLM is a codec-aware tokenization framework for VLM that replaces dense RGB encoding with lightweight structured representations derived from codec primitives. Token -93% / time-to-first-token -86%! Code announced💙 👉Review https://t.ly/3_GqN 👉Paper https://arxiv.org/pdf/2602.13191 👉Project https://sayands.github.io/cope/ 👉Repo TBA

17 119

🥝Conversational Segmentation🥝 👉CIS grounds abstract, intent-oriented concepts into pixel-accurate masks, reasoning about affordances, physics, and functional properties. Code/Demo released💙 👉Review https://t.ly/SsG57 👉Paper arxiv.org/pdf/2602.13195 👉Project glab-caltech.github.io/converseg/ 👉Repo github.com/AadSah/ConverSeg 👉Demo glab-caltech.github.io/converseg/#interactive-demo

17 119

🪿Teaching AI to illusions🪿 👉Stroke of Surprise by NYCU is a novel generative framework that optimizes vector strokes to satisfy distinct semantic interpretations at different drawing stages. As strokes are progressively added, the sketch reveals a completely different subject. Code released💙 👉Review https://t.ly/98Oim 👉Paper https://lnkd.in/dTA7iuce 👉Project https://lnkd.in/dhTMGw23 👉Repo https://lnkd.in/deQyDGFu

17 119

🫧SurfPhase: 3D Interfacial Dynamics🫧 👉SurfPhase is a novel model for reconstructing 3D interfacial dynamics from sparse camera views. Repo/Dataset announced💙 👉Review https://t.ly/g2P5F 👉Paper https://arxiv.org/pdf/2602.11154 👉Project https://yuegao.me/SurfPhase/ 👉Repo github.com/yuegao/SurfPhase