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 17 262 مشتركاً، محتلاً المرتبة 7 491 في فئة التكنولوجيات والتطبيقات والمرتبة 2 128 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 262 مشتركاً.
بحسب آخر البيانات بتاريخ 23 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 255، وفي آخر 24 ساعة بمقدار 2، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 18.93%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 7.17% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 3 267 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 237 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 20.
- 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 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
17 262
المشتركون
+224 ساعات
+67 أيام
+25530 أيام
أرشيف المشاركات
🌵Instance-Level Video Generation🌵
👉InstanceV is the first video generation framework to be designed specifically for instance-level control at the architectural level. Code & Data announced💙
👉Review https://t.ly/y_TBT
👉Paper arxiv.org/pdf/2511.23146
👉Project aliothchen.github.io/projects/InstanceV/
👉Repo TBA
🕶️ Seeing without Pixels 🕶️
👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙
👉Review https://t.ly/Ymd1c
👉Paper arxiv.org/pdf/2511.21681
👉Project sites.google.com/view/seeing-without-pixels
🕶️ Seeing without Pixels 🕶️
👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? #Google Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅CamFormer: pre-trained dedicated trajectory encoder
✅Proposing a novel contextualized trajectory encoding
✅Scenarios: egocentric & exocentric (third-person)
✅Outperforming "heavy" vision models in key scenarios
#artificialintelligence #AI #deeplearning #AIwithPapers #LLM
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2511.21681
👉Project https://sites.google.com/view/seeing-without-pixels
🔥 Smell Like Vision Spirit 🔥
👉New York Smells is a novel large-scale dataset of paired vision and olfaction captured in-the-wild, enabling the new task of cross-modal learning between smell and sight. With the lights out, it's less dangerous. Dataset available💙
👉Review https://t.ly/Ycn_B
👉Paper arxiv.org/pdf/2511.20544
👉Project smell.cs.columbia.edu/
🍓MotionV2V: Editing Motion in Video🍓
👉 Google unveils motion edits, a new approach for editing videos by controlling the change in motion from the original to the edited video using diffusion models. Impressive results. Repo released soon💙
👉Review https://t.ly/s0sIT
👉Paper https://arxiv.org/pdf/2511.20640
👉Project https://ryanndagreat.github.io/MotionV2V/
👉Repo https://github.com/RyannDaGreat/MotionV2V
🌩️ Cloud4D in time 🌩️
👉Cloud4D: physically-realistic 3D cloud fields using ground-based cameras at a 25 m spatial resolution and 5 s temporal resolution. Repo coming, Data released💙
👉Review https://t.ly/w7Zly
👉Paper arxiv.org/pdf/2511.19431
👉Project cloud4d.jacob-lin.com/
👉Repo TBD
👉Data https://drive.google.com/drive/folders/1QU_0kIUXIVt8h3uqygBeaF3Gvr_L5SdX?usp=drive_link
🧪EfficientSAM3 is out 🧪
👉Bristol announces EfficientSAM3, a family of efficient models built on Progressive Hierarchical Distillation (PHD) that transfers capability from SAM3 to lightweight students. Code coming (in sync with SAM3 release)💙
👉Review https://t.ly/bfXP2
👉Paper https://arxiv.org/pdf/2511.15833
👉Project https://simonzeng7108.github.io/efficientsam3/
👉Repo https://github.com/SimonZeng7108/efficientsam3
🦞Single Synthetic Image per Class🦞
👉MIT unveils Linear Gradient Matching (H/T Torralba), a novel method of distillation to use a single synthetic image per class for linear classifiers training (and more). Repo available💙
👉Review https://t.ly/dD3un
👉Paper arxiv.org/pdf/2511.16674
👉Project linear-gradient-matching.github.io/
👉Repo github.com/GeorgeCazenavette/linear-gradient-matching
🍕 Upsample Anything 🍕
👉Upsample Anything, a novel universal, training-free up-sampler via lightweight test-time optimization. No code but it's a relevant paper💙
👉Review https://t.ly/7LE6G
👉Paper https://lnkd.in/dsUfdtih
🍯Unwrapping of 3D Meshes🍯
👉PartUV is a novel part-based UV unwrapping method for 3D meshes; it combines learned part priors with geometric cues to generate a compact set of part-aligned charts. Repo released💙
👉Review https://t.ly/8dNIY
👉Paper arxiv.org/pdf/2511.16659
👉Project www.zhaoningwang.com/PartUV/
👉Repo github.com/EricWang12/PartUV
🔥 SAM 3/3D are OUT!! 🔥
👉#META released the SAM 3 family, a unified model for detection, segmentation, and tracking of objects in images & video using text, exemplar & visual prompts. Repo/Models under proprietary SAM license💙
👉Review https://t.ly/lnRZN
👉Paper https://t.ly/5tq9N
👉Project https://ai.meta.com/sam3/
👉Demo: https://segment-anything.com
👉Repo https://github.com/facebookresearch/sam3
⌚ Multi-Shot Video Segmentation ⌚
👉Fudan focuses on an underexplored task of multi-shot video object segmentation (MVOS). Benchmark and repo available (the extension part of SAM) under Apache 2.0💙
👉Review https://t.ly/WBW00
👉Paper https://arxiv.org/pdf/2511.13715
👉Project https://henghuiding.com/SAAS/
👉Repo https://github.com/FudanCVL/SAAS
🌩️ It's "Time-to-Move" 🌩️
👉Technion + Nvidia Time-to-Move (TTM) is a training-free, plug-and-play framework for motion- and appearance-controlled video generation with I2V diffusion models (Wan 2.2, CogVideoX, & Stable VD). Impressive results!
👉Review https://t.ly/0pwXm
👉Paper https://lnkd.in/dxD3uHYb
👉Project https://lnkd.in/dcE5juyM
👉Repo https://lnkd.in/dMMUjybJ
🔥Depth Anything 3 is out🔥
👉#ByteDance unveils Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from arbitrary visual inputs, with or without known camera poses. Repo under Apache 2.0💙
👉Review https://t.ly/AOPu7
👉Paper arxiv.org/pdf/2511.10647
👉Project https://lnkd.in/dnByyn2z
👉Repo https://lnkd.in/daCVz_4a
👉Demo https://lnkd.in/dKUZiJt
🟩 Foundational Humanoid 🟩
👉#NVIDIA unveils SONIC a novel foundational model for high-precision teleoperation & interactive control capabilities (running, jumping, crawling) with natural human-like movements. Code announced💙
👉Review https://t.ly/aUx_U
👉Paper https://lnkd.in/dctfShu8
👉Project https://lnkd.in/d_inmA2p
🚨 Announcement 🚨
I’ve received numerous reports of people blatantly copying my content on LinkedIn just to get a few likes.
Let me be very clear: I put a great deal of time and effort into reviewing papers and creating original, meaningful content. It’s disappointing to see professionals (some of whom are even members of this group or my connections) resorting to plagiarism instead of contributing their own ideas.
👉 Starting today, I’ll be removing these connections from LinkedIn and banning such individuals from this group.
📢 I also encourage everyone to report these cases whenever you come across them. Every single report helps stop this bad habit and keeps our community fair, respectful, and authentic.
🐼Pixel-Dense Embedding of Motion🐼
👉FlowFeat is a novel high-resolution and multi-task feature representation that embeds a distribution of plausible apparent motions, or motion profiles. Repo available under 💙
#artificialintelligence #AI #deeplearning #AIwithPapers
👉Review https://t.ly/aUx_U
👉Paper arxiv.org/pdf/2511.07696
👉Project tum-vision.github.io/flowfeat
👉Repo github.com/tum-vision/flowfeat
🎸Another BRIXEL in the Wall 🎸
👉BRIXEL allows the user to produce high-resolution feature maps using the DINOv3 backbone without requiring large amounts of compute. Repo released💙
👉Review https://t.ly/fZPwC
👉Paper arxiv.org/pdf/2511.05168
👉Repo github.com/alexanderlappe/BRIXEL
