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

📈 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 17 142 مشتركاً، محتلاً المرتبة 7 723 في فئة التكنولوجيات والتطبيقات والمرتبة 2 241 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 142 مشتركاً.

بحسب آخر البيانات بتاريخ 23 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -190، وفي آخر 24 ساعة بمقدار -2، مع بقاء الوصول العام مرتفعاً.

حالة التحقق: غير موثّقة
معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 25.09‎%. 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 4 302 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 177 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 24 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

17 142

المشتركون

-224 ساعات

-367 أيام

-19030 أيام

4 302

عرض المشاهدات

~ 1 17724 ساعات

~ 1 35348 ساعات

25.09%

معدل المشاركة

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المشاركات في اليوم

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أرشيف المشاركات

17 137

🦀 simPLE: learning to grasp only with CAD 🦀 👉simPLE learns to pick, regrasp & place objects precisely, given only the object CAD model and no prior experience 😎Review https://t.ly/ab5pA 😎Paper arxiv.org/pdf/2307.13133.pdf 😎Project mcube.mit.edu/research/simPLE.html

17 137

🥬 Generative AI’s Next Frontiers 🥬 👉Hair simulation, 2D->3D animation, and much more. ~20 papers from #NVIDIA accepted into #SIGGRAPH2023 😎 Review https://t.ly/wgGin

17 137

🛵ALPR via CTS-Matching 🛵 👉UIT unveils a neural approach (#YOLO5 + tracking + rotation) to improve the license plate recognition accuracy 😎Review https://t.ly/VP4BP 😎Paper arxiv.org/pdf/2307.11336.pdf 😎Code github.com/chequanghuy/Character-Time-series-Matching

17 137

🪛 CAD-based Object Segmentation 🪛 👉 A novel three-stage approach to segment unseen objects in RGB images using their CAD models 😎Review https://t.ly/RtHLN 😎Paper arxiv.org/pdf/2307.11067.pdf 😎Code https://github.com/nv-nguyen/cnos

17 137

🪤 PAPR: Proximity Attention Point Render 🪤 👉PAPR: fast point-based scene representation with differentiable renderer approach 😎Review https://t.ly/yoI0g 😎Paper arxiv.org/pdf/2307.11086.pdf 😎Project https://zvict.github.io/papr

17 137

🪤 PAPR: Proximity Attention Point Render 🪤 👉PAPR: fast point-based scene representation with differentiable renderer approach

17 137

💪 Muscles in Action with #AI 💪 👉Muscles in Action (MIA): learn to incorporate muscle activity into human motion representations 😎Review https://t.ly/hUKub 😎Paper arxiv.org/pdf/2212.02978.pdf 😎Project musclesinaction.cs.columbia.edu

17 137

👩‍🦰 Ultra-Realistic Neural Hair 👩‍🦰 👉A novel method to reconstruct the hair geometry at a strand level from monocular video or multi-view images 😎Review https://t.ly/6xZyp 😎Paper arxiv.org/pdf/2306.05872.pdf 😎Project samsunglabs.github.io/NeuralHaircut 😎Code github.com/SamsungLabs/NeuralHaircut

17 137

🪟 META's Ultra-Realistic Data for #AR🪟 👉Aria Digital Twin: egocentric dataset for object detection/tracking, reconstruction/understanding, S2R learning, human pose prediction and more 😎Review https://t.ly/MRPt1 😎Paper arxiv.org/pdf/2306.06362.pdf 😎Project www.projectaria.com/datasets/adt/ 😎Code github.com/facebookresearch/projectaria_tools

17 137

🍉 AltFreezing: new SOTA in detecting fake-faces 🍉 👉#Microsoft unveils AltFreezing: spatial/temporal artifacts in one model for more general face forgery detection 😎Review https://t.ly/mkIKX 😎Paper https://t.ly/z4KnJ 😎Code github.com/ZhendongWang6/AltFreezing

17 137

🦙 Llama-2: the Open-Source "#chatgpt"🦙 👉GenAI, #Meta unveils Llama-2: a collection of LLMs ranging in scale 7-70B parameters. Challenging with #chatgpt, but open. 😎Review https://t.ly/bLJgP 😎Paper https://t.ly/AOXru 😎Project https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/

17 137

☔ #SelfDriving? It's all about weather! ☔ 👉Novel self-supervised MDE method to handle adverse weather in real-world autonomous driving 😎Review https://t.ly/tcLQW 😎Paper arxiv.org/pdf/2307.08357.pdf 😎Project kieran514.github.io/Robust-Depth-Project/

17 137

🐈 Gen-AI as representation learner 🐈 👉DreamTeacher: novel self-supervised feats. representation learning framework that utilizes gen-nets for pre-training downstream image backbones 😎Review https://t.ly/RL8iG 😎Paper arxiv.org/pdf/2307.07487.pdf 😎Project research.nvidia.com/labs/toronto-ai/DreamTeacher

17 137

🧯 Neural Focal Modulation for VAR 🧯 👉Video-FocalNet is a novel architecture for video recognition that models both local and global context 😎Review https://t.ly/rF_fk 😎Paper arxiv.org/pdf/2307.06947.pdf 😎Project talalwasim.github.io/Video-FocalNets 😎Code github.com/TalalWasim/Video-FocalNets

17 137

💡DATID-3D: Diffusive Text-to-3D Generation💡 👉 A novel domain adaptation method for 3D via text-to-image diffusion. 🤗-Demo available! 😎Review https://t.ly/ecBvM 😎Paper arxiv.org/pdf/2211.16374.pdf 😎Project gwang-kim.github.io/datid_3d/ 😎Code github.com/gwang-kim/DATID-3D 🤗Demo huggingface.co/spaces/gwang-kim/DATID-3D 😎Colab colab.research.google.com/drive/1e9NSVB7x_hjz-nr4K0jO4rfTXILnNGtA?usp=sharing

17 137

🎪 Extreme Human Pose Estimation 🎪 👉RePoGen: novel synthetic data generator of extreme/realistic poses of humans 😎Review https://t.ly/ecBvM 😎Paper arxiv.org/pdf/2307.06737.pdf 😎Project mirapurkrabek.github.io/RePoGen-paper 😎Code github.com/MiraPurkrabek/RePoGen

17 137

🃏 Deepfake via casual self-scan 🃏 👉TAU presents a novel approach to reenact an ID using only a casual self-scan 😎Review https://t.ly/9T8Wi 😎Paper arxiv.org/pdf/2307.06307.pdf 😎Project arielazary.github.io/PGR

17 137

🔥o-TTT: Test-Time Training on fire 🔥 👉Extending the TTT to the streaming setting. Suitable for Panoptic, Instance & Colorization. 😎Review https://t.ly/eZYA 😎Paper arxiv.org/pdf/2307.05014.pdf 😎Project https://video-ttt.github.io/ 😎Code github.com/renwang435/video-ttt-release

17 137

🍡 Text2Cinemagraphs: Cinemagraph from text 🍡 👉CMU (+ #Snap) unveils a fully automated method for creating cinemagraphs from text descriptions 😎Review https://t.ly/BwZs6 😎Paper arxiv.org/pdf/2307.03190.pdf 😎Project text2cinemagraph.github.io/website/ 😎Code github.com/text2cinemagraph/text2cinemagraph

17 137

🛣️ STAR.: 3D-tracking w/ attention paradigm 🛣️ 👉#Mercedes STAR: e2e 3D object tracking that follows the tracking-by-attention paradigm 😎Review https://t.ly/JoGj 😎Paper arxiv.org/pdf/2306.17602.pdf 😎Project simondoll.github.io/publications/star_track