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AI with Papers - Artificial Intelligence & Deep Learning

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 17 144 مشتركاً، محتلاً المرتبة 7 701 في فئة التكنولوجيات والتطبيقات والمرتبة 2 225 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 144 مشتركاً.

بحسب آخر البيانات بتاريخ 25 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -186، وفي آخر 24 ساعة بمقدار 3، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 23.94‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 6.86‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 0 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 177 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 0.
  • 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 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

17 144
المشتركون
+324 ساعات
-367 أيام
-18630 أيام
أرشيف المشاركات
🌪️ 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

🦉PANDORA: Polarized Neural Decomposition🦉 👉CIL lab unveils PANDORA: polarimetric inverse rendering approach via INR 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Geometry, reflectance & illumination ✅normal, signed distance field, mesh ✅Diffuse-specular separation ✅Hi-fI incident illumination More https://bit.ly/3CzGp3F

🍈 #StableDiffusion archive is out🍈 👉Lexica art is a Stable Diffusion prompt search engine. Real-time, countless #stablediffusion results for everyone. I had fun with the GOAT, #Maradona. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Maradona scoring against a capybara... ✅A poster of space jam with Maradona... ✅Painting of Maradona very detailed... ✅Painting of Maradona in heaven... More: https://bit.ly/3PTXHLH

🌐RelPose: Probabilistic Relative Pose🌐 👉A novel method for core component in #SLAM / NeRF-powered apps. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Core component of SfM/SLAM ✅Pre-processing for neural (NeRF) ✅Energy-based over rotations ✅SOTA on both seen/unseen objects More: https://bit.ly/3T60TXw

🍈DeepBillboards: old-school trick for #VR🍈 👉DeepBillboards models a 3D object implicitly using neural net on the user’s viewing direction 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅#Google Brain +Tsukuba + Tokyo ✅Rendering at higher res., improving #VR ✅NeRF into interactive VR with accuracy++ ✅NeRF (or any others) directly in #Unity More: https://bit.ly/3CsTQ5y

🥭Massive GTA-V human dataset🥭 👉GTA-Human: outperforming SOTA with a purely synthetic training. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅600+ gender, age, ethnicity & clothing ✅20,000+ clips, variety of human activities ✅6 categories of location, different BGs ✅Occlusions, lighting, and weather system More: https://bit.ly/3wpZyRD

🔥 KeypointNeRF: code is out! 🔥 👉KeypointNeRF by #Meta: "NeRF"-avatars 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Generalizable NeRF for virtual avatar ✅Sparse 3D keypoints for SOTA avatar ✅Novel unseen subjects from 2/3 views ✅"iPhone" captures for #metaverse More: https://bit.ly/3pyl17e

🥑 CLIP-based Neural Style Transfer 🥑 👉From #Nvidia a novel method for transferring the style to a #3D object 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Texture style for 3D by CLIP-ResNet50 ✅Nearest-neighbor feature matching loss ✅CLIP-based loss extraction of textures ✅NNFM for multiple style pics / control ✅No source code or models available 😒 More: https://bit.ly/3c32dK5

🍏NeuMan: Human NeRF in the wild🍏 👉#Apple opens a novel human pose/view from just a single in-the-wild video 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅No extra devices/annotations ✅Both Human (novel poses) + Scene ✅E2E SMPL optimization + error-corr. ✅Applications such as "telegathering" More: https://bit.ly/3K4iTO6

🧰 FGT: flow-guided inpainting 🧰 👉#Microsoft (+USTC) unveils FGT: flow-guided ViT for video inpainting 🤯 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅OF into transformer for attention++ ✅Flow completion net w/ local feats. ✅Dual perspective spatial MHSA ✅Local attention with global content More: https://bit.ly/3pk5J5S