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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.

17 261
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أرشيف المشاركات
💜 #Selfdriving in 80's. Damn Romantic 💜 👉The first self-driving car with people on board, 1986. So slow and lovely. More: https://bit.ly/3BtRDon

🥤K-VIL: Keypoint-based visual imitation🥤 👉K-VIL: auto-incremental extraction of object-centric task representation. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Efficient task-relevant keypoints ✅Embodiment-independent tasks ✅Adaptation of tasks to new scenes ✅Input: only a small set of demo clips ✅Novel keypoint-based controller More: https://bit.ly/3eIrxpP

🐲 Open-Source Self-Driving projects 🐲 👉A free repo with many autonomous vehicle-related projects 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Basic/Advance Lane/Line Detection ✅Driving behavior by training & validating ✅Autopilot: predicting steering angle More: https://bit.ly/3qqJ7RB

🎪 SOTA in Arbitrary Shape Text Detection 🎪 👉Novel unified coarse-to-fine Transformer for arbitrary shape text detection 𝐇
🎪 SOTA in Arbitrary Shape Text Detection 🎪 👉Novel unified coarse-to-fine Transformer for arbitrary shape text detection 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Coarse-to-fine arbitrary text detection ✅Accurate text detection, NO post-process ✅Boundary proposal generation mechanism ✅Innovative boundary transformer (iterative) ✅Boundary energy loss (BEL) for refinement More: https://bit.ly/3D6Ryt4

👹TT-GNeRF: generative NeRF for Faces👹 👉TT-GNeRF: a novel 3D-aware GANs based on generative NeRF for faces 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅ETH + Uni_Trento + #Snap 🤯 ✅DAEM for disentanglement of 3D model ✅"Training-as-Init, Optimizing-for-Tuning" ✅Consistency++, preserving non-target ROI ✅Unsupervised optimization of geometry More: https://bit.ly/3ARZmMw

🌈 X-NeRF: Cross-Spectral NeRF 🌈 👉Cross-Spectral NeRF from cams with different light spectrums 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅First ever cross-spectral NeRF ✅Avoiding non-trivial calib/match ✅Normalized Cross-Device Coords ✅Novel dataset w/ RGB, MS, & IR More: https://bit.ly/3RqHnUo

🐠VIS - Deformable Transformers 🐠 👉DeVIS: VIS method with efficiency and performance of deformable ViT 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Temp. multi-scale D-Attention ✅Instance-aware object queries ✅Mask: DA + multi-scale feats map ✅Improved multi-cue clip tracking ✅SOTA on YouTube-VIS 2021/OVIS More: https://bit.ly/3TQv1Xc

🤯 #StableDiffusion + #Dallemini = BOOM! 🤯 👉A #colab notebook that combines Stable Diffusion + DALL-E Mini (Craiyon) More:
🤯 #StableDiffusion + #Dallemini = BOOM! 🤯 👉A #colab notebook that combines Stable Diffusion + DALL-E Mini (Craiyon) More: https://bit.ly/3TTOshR

🦎 VMT: Video Mask Transfiner 🦎 👉Novel highly efficient ViT structure for video instance segmentation. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅HD & more temporally stable mask ✅Higher resolution features for VIS ✅Detecting error-prone s-t. regions ✅Auto-refinement on training data! More: https://bit.ly/3RKXtb4

🫐 Stable Diffusion Video is out! 🫐 👉A free notebook to generate videos by interpolating the latent space of SD. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Blueberry to strawberry spaghetti ✅Dream items from same prompt ✅Morph different prompts (seeds) ✅Built on a script by A. Karpathy More: https://bit.ly/3ey8632

🌪️ TimeLapse++: Video Temporal Pyramid🌪️ 👉Multi-scale lens to view the passage of time: far beyond a "classic" timelapse 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Inspired by "old-school" spatial pyramids ✅Video Spectrogram to go through pyramid ✅Months/years of data in a few seconds! ✅Multi-temporal freq., no aliasing More: https://bit.ly/3TKnYPS

🥑 DALL·E: Outpainting via #NLP 🥑 👉Extending any original image, creating large-scale images in any aspect ratio 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Extending an image beyond its borders ✅Visual elements in same style of the input ✅Driving the image "story" in new directions ✅Shadows, reflections & textures w/ context More: https://bit.ly/3eoH8uD

🪨Controllable #3D Adversarial Face🪨 👉#Meta (+CMU) on decoupling identity/expression + granular control over expressions 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Supervised auto-enc. + GAN ✅UV texture maps + 3D faces ✅Control expression, saving ID ✅Code under X11 License More: https://bit.ly/3AVE80q

🚗 Massive Dataset in Virtual Cities 🚗 👉Synthehicle: 7 hours of labeled material, 340 cams, 64 days, rain, dawn, & night scenes. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Multi-target multi-cam tracking ✅2D, 3D, segm. & depth annotations ✅Instance, semantic & panoptic segm. ✅340 clips, 64 scenes, 17 hrs, 4M BBs More: https://bit.ly/3TArHiV

🧡 Avatarization in 90's. So Romantic 🧡 👉Making of the first #MortalKombat in early 90's More: https://bit.ly/3wTSpJB

🍊StableFace: Talking Face Generation🍊 👉Analysis on motion jittering in 3D face generation (audio-in -> video-out) 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Motion jittering analysis for stability ✅Gaussian-based adaptive smoothing ✅Augmented erosions of neural renderer ✅Audio-fused generator for dependency More: https://bit.ly/3Kt95gI

🎍#3D scene manipulation from 2D🎍 👉Reconstruct, decompose, manipulate & render 3D scenes in a single pipeline 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Unique 3D, non-occupied space from 2D ✅Inverse query algorithm for shapes ✅First synthetic dataset for 3D editing More: https://bit.ly/3RlYhTY

🔵 Deep Saliency: driving the attention 🔵 👉Google unveils a family of operators to "drive" human saliency 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Editing image to drive Saliency ✅Transforms to hide distractors ✅Warping operator for distractor ✅GAN-op for less-saliency altern. More: https://bit.ly/3KoQQc2

🔥 #AIwithPapers: we are 4,000+! 🔥 💙💛Lot of people joined, and we talked about #StableDiffusion only twice! Can't believe it.💙💛 😈 Invite your friends -> https://t.me/AI_DeepLearning

🔥IDOL (#CVPR2022 winner): code is out!🔥 👉IDOL for VIS: outperforming all online/offline methods, the new SOTA! 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Online usually inferior by >10AP ✅Online based on contrast-learning ✅Discriminative++ instance embeddings ✅Full exploiting history for stability More https://bit.ly/3dXCDXw