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 17 236 مشتركاً، محتلاً المرتبة 7 687 في فئة التكنولوجيات والتطبيقات والمرتبة 2 248 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 236 مشتركاً.
بحسب آخر البيانات بتاريخ 03 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -80، وفي آخر 24 ساعة بمقدار -13، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 13.16%. 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 2 271 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 14.
- 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 04 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
17 236
المشتركون
-1324 ساعات
-677 أيام
-8030 أيام
أرشيف المشاركات
🔍 Nvidia Locate Anything 🔍
👉Diverse localization tasks under a unified vision-language model, including document understanding, GUI grounding, dense detection, and OCR. Repo released💙
👉Review https://t.ly/PvwFo
👉Paper https://lnkd.in/dWfNpzPZ
👉Project https://lnkd.in/dM89BX-8
👉Repo https://lnkd.in/dC4KCQSM
🪔Latent Decoding with Pixel Diffusion🪔
👉PiD by Nvidia is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass. Repo under Apache 2.0💙
👉Review https://t.ly/y19mA
👉Paper https://lnkd.in/duVC25C2
👉Project https://lnkd.in/dW6TkzCB
👉Repo https://lnkd.in/dnGdgKRr
🍒Count Anything, Any Granularity🍒
👉Open-world counting as multi-grained counting, where visual exemplars specify target appearance and fine-grained text specifies the intended semantic granularity across five explicit levels. Repo/Data under Apache💙
👉Review https://t.ly/nqz80
👉Paper https://lnkd.in/dp7khTRU
👉Project https://lnkd.in/d_jfX_Yn
👉Repo https://lnkd.in/dkTRGZkG
👉Data https://lnkd.in/dB83jRyT
🦄Unified Correspondence Transformer🦄
👉UniCorrn is the first correspondence model with shared weights that unifies 2D-2D, 2D-3D, and 3D-3D geometric matching with an end-to-end transformer architecture. Repo under CC BY-NC-SA 4.0💙
👉Review https://t.ly/2OBdq
👉Paper https://arxiv.org/pdf/2605.04044
👉Project https://neu-vi.github.io/UniCorrn/
👉Repo https://github.com/neu-vi/UniCorrn
About the frequency of posting in the channel:
🪝Syn4D: Multiview Synthetic 4D Dataset🪝
👉Syn4D is novel multi-view synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations💙
👉Review https://t.ly/SL1mk
👉Paper https://arxiv.org/pdf/2605.05207
👉Project https://jzr99.github.io/Syn4D/
👉Repo https://github.com/jzr99/Syn4D
👉Data huggingface.co/datasets/Syn4D/Syn4D_RGBD/tree/main
🧘♀️Holistic Shot Boundary Detection🧘♀️
👉OmniShotCut detects shot changes of the video in diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.), and recognize Sudden Jump and Transitions (dissolve, fade, wipe, etc.) by proposing a Shot-Query-based Video Transformer. Repo, demo & benchmark💙
👉Review https://t.ly/sTi7N
👉Paper https://arxiv.org/pdf/2604.24762
👉Project uva-computer-vision-lab.github.io/OmniShotCut_website/
👉Repo github.com/UVA-Computer-Vision-Lab/OmniShotCut
🛒 Reshoot-Anything is out 🛒
👉Reshoot-Anything reshoots dynamic monocular videos under novel camera trajectories. Code under Apache 2.0 💙
👉Review https://t.ly/MIqAc
👉Paper https://arxiv.org/pdf/2604.21776
👉Project adithyaiyer1999.github.io/reshoot-anything/
👉Repo github.com/morphicfilms/video-to-video
💙 PY4AI 2026: here we are! 💙
👉The third edition of our conference is official! Speaker list and (free) tickets: https://t.ly/L4_52
🎈Face Anything 4D (SOTA)🎈
👉A novel unified 4D facial reconstruction and dense tracking from image sequences: new SOTA in facial single-image and mono-video depth estimation, dense 4D reconstruction, and 3D point tracking. Repo & Dataset announced💙
👉Review https://t.ly/zItie
👉Paper https://arxiv.org/pdf/2604.19702
👉Project kocasariumut.github.io/FaceAnything
👉Repo TBA
🌗Mobile Ultra-detailed Avatars🌗
👉Given skeletal poses and a virtual camera as inputs, MUA by Max Planck Institute produces photorealistic renderings and hyper-detailed geometry of animatable clothed humans. Repo announced💙
👉Review https://t.ly/QPCy6
👉Paper https://arxiv.org/pdf/2604.18583
👉Project https://vcai.mpi-inf.mpg.de/projects/MUA/
👉Repo TBA
👩🦰 3D Head w/ Deformable Hair 👩🦰
👉Xi’an Jiaotong University unveils a novel method that reconstructs decoupled 3D Gaussian head avatars from a single input image: effortless hairstyle transfer with natural dynamic hair motion. Code announced💙
👉Review https://t.ly/kWZdd
👉Paper https://arxiv.org/pdf/2604.14782
👉Project yuansun-xjtu.github.io/CompHairHead.io/
👉Repo yuansun-xjtu.github.io/CompHairHead.io/
🐞GCT 3D Reconstruction🐞
👉ANT unveils LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. Repo under A-NC 4.0 International💙
👉Review https://t.ly/ExodA
👉Paper https://arxiv.org/pdf/2604.14141
👉Project https://arxiv.org/pdf/2604.14141
👉Repo github.com/robbyant/lingbot-map
📱3D Human-Object Contact📱
👉Pi-HOC by CMU + NREC is a novel single-pass, instance-aware framework for dense 3D semantic contact prediction of all human-object pairs. Repo announced💙
👉Review https://t.ly/TAgG1
👉Paper https://arxiv.org/pdf/2604.12923
👉Project https://pi-hoc.github.io/
👉Repo https://github.com/SravanChittupalli/Pi-HOC
🐓Interactive Objects from EgoVideo🐓
👉EgoFun3D by Simon Fraser University is a coordinated task, dataset and benchmark for modeling interactive 3D objects from egocentric videos. Repo (TBA), demo & dataset💙
👉Review https://t.ly/YhGN7
👉Paper arxiv.org/pdf/2604.11038
👉Project 3dlg-hcvc.github.io/EgoFun3D/
👉Repo github.com/3dlg-hcvc/EgoFun3D
👉Demo bc79fea884062374b3.gradio.live/
🧴OmniShow: Automatic Contents Creation🧴
👉OmniShow is the novel SOTA in content creation with industry-grade performance. Impressive results, best with audio. Repo announced💙
👉Review https://t.ly/Pm-7U
👉Paper arxiv.org/pdf/2604.11804
👉Project correr-zhou.github.io/OmniShow/
👉Repo github.com/Correr-Zhou/OmniShow
🔥SOTA 3D Detection in the wild🔥
👉WildDet3D is a novel unified geometry-aware architecture that natively accepts text, point, and box prompts and can incorporate auxiliary depth signals at inference time. New SOTA! Repo, models & #iphone💙
👉Review https://t.ly/8NxBN
👉Paper https://arxiv.org/pdf/2604.08626
👉Project https://allenai.github.io/WildDet3D/
👉Repo https://github.com/allenai/WildDet3D
🐞6D Object Pose w/ Deformation🐞
👉DeSOPE by Xidian & #MagicLeap is a novel large-scale dataset for 6DoF deformed objects: 665K pose annotations produced via a semiautomatic pipeline. Repo & Dataset announced💙
👉Review https://t.ly/M5VgX
👉Paper https://arxiv.org/pdf/2604.06720
👉Project https://desope-6d.github.io/
👉Repo TBA
🪞1.1M Metric VTON Dataset🪞
👉Google's Fit-Inclusive Try-on: large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. Repo & dataset announced💙
👉Review https://t.ly/cs-pt
👉Paper arxiv.org/pdf/2604.08526
👉Project johannakarras.github.io/FIT/
👉Repo TBA
Here the preview, tomorrow the full clip from official source :)
