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 17 166 مشتركاً، محتلاً المرتبة 7 718 في فئة التكنولوجيات والتطبيقات والمرتبة 2 234 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 166 مشتركاً.

بحسب آخر البيانات بتاريخ 20 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -169، وفي آخر 24 ساعة بمقدار 0، مع بقاء الوصول العام مرتفعاً.

حالة التحقق: غير موثّقة
معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 22.86‎%. 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 3 926 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 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 21 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

17 166

المشتركون

لا توجد بيانات24 ساعات

-357 أيام

-16930 أيام

3 926

عرض المشاهدات

لا توجد بيانات24 ساعات

لا توجد بيانات48 ساعات

22.86%

معدل المشاركة

لا توجد بيانات

المشاركات في اليوم

Ads index

beta

أرشيف المشاركات

17 157

🫠 X-Portrait 2: SOTA(?) Portrait Animation 🫠 👉ByteDance unveils a preview of X-Portrait2, the new SOTA expression encoder model that implicitly encodes every minuscule expressions from the input by training it on large-scale datasets. Impressive results but no paper & code announced. 👉Review https://t.ly/8Owh9 [UPDATE] 👉Paper ? 👉Project byteaigc.github.io/X-Portrait2/ 👉Repo ?

17 157

🧠 Single Neuron Reconstruction 🧠 👉SIAT unveils NeuroFly, a framework for large-scale single neuron reconstruction. Formulating neuron reconstruction task as a 3-stage streamlined workflow: automatic segmentation - connection - manual proofreading. Bridging computer vision and neuroscience 💙 👉Review https://t.ly/Y5Xu0 👉Paper https://arxiv.org/pdf/2411.04715 👉Repo github.com/beanli161514/neurofly

17 157

💪 Muscles in Time Dataset 💪 👉Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. MinT contains 9+ hours of simulation data covering 227 subjects and 402 simulated muscle strands. Code & Dataset available soon 💙 👉Review https://t.ly/108g6 👉Paper arxiv.org/pdf/2411.00128 👉Project davidschneider.ai/mint 👉Code github.com/simplexsigil/MusclesInTime

17 157

🏣 CityGaussianV2: Large-Scale City 🏣 👉A novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency: 10x compression, 25% faster & -50% memory! Source code released💙 👉Review https://t.ly/Xgn59 👉Paper arxiv.org/pdf/2411.00771 👉Project dekuliutesla.github.io/CityGaussianV2/ 👉Code github.com/DekuLiuTesla/CityGaussian

17 157

☀️ Universal Relightable Avatars ☀️ 👉#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results! 👉Review https://t.ly/U-ESX 👉Paper arxiv.org/pdf/2410.24223 👉Project junxuan-li.github.io/urgca-website

17 157

🍜 REM: Segment What You Describe 🍜 👉REM is a framework for segmenting concepts in video that can be described via LLM. Suitable for rare & non-object dynamic concepts, such as waves, smoke, etc. Code & Data announced 💙 👉Review https://t.ly/OyVtV 👉Paper arxiv.org/pdf/2410.23287 👉Project https://miccooper9.github.io/projects/ReferEverything/

17 157

🔥🔥 The code is out 🔥🔥 👉Code https://github.com/HaixinShi/fmov_pose

17 157

🔥 D-FINE: new SOTA Detector 🔥 👉D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR model. New SOTA on MS COCO with additional data. Code & models available 💙 👉Review https://t.ly/aw9fN 👉Paper https://arxiv.org/pdf/2410.13842 👉Code https://github.com/Peterande/D-FINE

17 157

🫐 Blendify: #Python + Blender 🫐 👉Lightweight Python framework that provides a high-level API for creating & rendering scenes with #Blender. It simplifies data augmentation & synthesis. Source Code released💙 👉Review https://t.ly/l0crA 👉Paper https://arxiv.org/pdf/2410.17858 👉Code https://virtualhumans.mpi-inf.mpg.de/blendify/

17 157

⛈️ SMITE: SEGMENT IN TIME ⛈️ 👉SFU unveil SMITE: a novel AI that -with only one or few segmentation references with fine granularity- is able to segment different unseen videos respecting the segmentation references. Dataset & Code (under Apache 2.0) announced 💙 👉Review https://t.ly/w6aWJ 👉Paper arxiv.org/pdf/2410.18538 👉Project segment-me-in-time.github.io/ 👉Code github.com/alimohammadiamirhossein/smite/

17 157

🌻 Plant Camouflage Detection🌻 👉PlantCamo Dataset is the first dataset for plant camouflage detection: 1,250 images with camouflage characteristics. Source Code released 💙 👉Review https://t.ly/pYFX4 👉Paper arxiv.org/pdf/2410.17598 👉Code github.com/yjybuaa/PlantCamo

17 157

🪁 PL2Map: efficient neural 2D-3D 🪁 👉PL2Map is a novel neural network tailored for efficient representation of complex point & line maps. A natural representation of 2D-3D correspondences 👉Review https://t.ly/D-bVD 👉Paper arxiv.org/pdf/2402.18011 👉Project https://thpjp.github.io/pl2map 👉Code https://github.com/ais-lab/pl2map

17 157

🧿 Look Ma, no markers 🧿 👉#Microsoft unveils the first technique for marker-free, HQ reconstruction of COMPLETE human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware. Impressive results! Repo for training & Dataset released💙 👉Review https://t.ly/5fN0g 👉Paper arxiv.org/pdf/2410.11520 👉Project microsoft.github.io/SynthMoCap/ 👉Repo github.com/microsoft/SynthMoCap

17 157

🔥BitNet: code of 1-bit LLM is out 🔥 👉BitNet by #Microsoft, announced in late 2023, is a 1-bit Transformer architecture designed for LLMs. BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Source Code just released a few hours ago 💙 👉Review https://t.ly/3G2LA 👉Paper arxiv.org/pdf/2310.11453 👉Code https://lnkd.in/duPADJVb

17 157

☀️ GS + Depth = SOTA ☀️ 👉ETH unveils DepthSplat, the new SOTA in depth estimation and novel view synthesis tasks. The key feature is the cross-task interactions between Gaussian Splatting & depth estimation. Source Code to be released in a few days💙 👉Review https://t.ly/87HuH 👉Paper arxiv.org/abs/2410.13862 👉Project haofeixu.github.io/depthsplat/ 👉Code github.com/cvg/depthsplat

17 157

🦠 Neural Metamorphosis 🦠 👉NU Singapore unveils NeuMeta to transform neural nets by allowing a single model to adapt on the fly to different sizes, generating the right weights when needed. 👉Review https://t.ly/DJab3 👉Paper arxiv.org/pdf/2410.11878 👉Project adamdad.github.io/neumeta 👉Code github.com/Adamdad/neumeta

17 157

🔥 CoTracker3 by #META is out! 🔥 👉#Meta (+VGG Oxford) unveils CoTracker3, a new tracker that outperforms the previous SoTA by a large margin using only the 0.1% of the training data 🤯🤯🤯 👉Review https://t.ly/TcRIv 👉Paper arxiv.org/pdf/2410.11831 👉Project cotracker3.github.io/ 👉Code github.com/facebookresearch/co-tracker

17 157

🪞Robo-Emulation via Video Imitation🪞 👉OKAMI (UT & #Nvidia) is a novel foundation method that generates a manipulation plan from a single RGB-D video and derives a policy for execution. 👉Review https://t.ly/_N29- 👉Paper arxiv.org/pdf/2410.11792 👉Project https://lnkd.in/d6bHF_-s

17 157

🔥 DEPTH ANY VIDEO is out! 🔥 👉DAV is a novel foundation model for image/video depth estimation.The new SOTA for accuracy & consistency, up to 150 FPS! 👉Review https://t.ly/CjSz2 👉Paper arxiv.org/pdf/2410.10815 👉Project depthanyvideo.github.io/ 👉Code github.com/Nightmare-n/DepthAnyVideo