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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
Больше📈 Аналитический обзор Telegram-канала AI with Papers - Artificial Intelligence & Deep Learning
Канал AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 17 146 подписчиков, занимая 7 718 место в категории Технологии и приложения и 2 244 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 146 подписчиков.
Согласно последним данным от 22 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -178, а за последние 24 часа — -15, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 24.30%. В первые 24 часа после публикации контент обычно набирает 6.86% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 4 167 просмотров. В течение первых суток публикация набирает 1 177 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 26.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как framework, object, dataset, tba, depth.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 23 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
17 146
Подписчики
-1524 часа
-437 дней
-17830 день
Архив постов
🔥 EfficientSAM: 20x faster Segment Anything 🔥
👉Meta AI Research unveils a novel family of SAM-like models, light-weight SAM models with SOTA quality-efficiency trade-offs. Up to 20x faster!
👉Review https://t.ly/966QS
👉Paper https://lnkd.in/duijp_Rh
👉Project https://lnkd.in/dW-p2CuH
👉Code https://lnkd.in/dAbZaB2t
👉Demo https://lnkd.in/d-tjKiUd
🩰 Magic Animating Human 🩰
👉MagicAnimate: the new SOTA in human animation. Code available: let's dance!
👉Review https://t.ly/Oq7Za
👉Paper https://lnkd.in/dSUbGgCs
👉Project https://lnkd.in/dkVFf-SV
👉Code https://lnkd.in/dj2dbzdg
👉Demo https://lnkd.in/dHEKPE9q
Hello everybody,
a lot of you asked me to re-open the sharing of the contents to involve more people. I want to follow your suggestion, hope you will enjoy this new mood!
👍 FREE TO FORWARD TO OTHER TELEGRAM CHANNELS
🔥 NO COPY OF THE POSTS
🔥 NO COMMERCIAL USAGE
🔥 NO UNRESPECTFUL USAGE
⚠️ UNDO THE FORWARDING OPTION AT THE FIRST VIOLATION ⚠️
🔎 Generative Powers of Ten 🔍
👉A text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene. From universe to a human cell 🤯
👉Review https://t.ly/2DG44
👉Paper https://lnkd.in/eDcSpU59
👉Project https://lnkd.in/e6NKu8n9
🍡 Animate Anyone: new SOTA! 🍡
👉Alibaba unveils Animate Anyone: novel #AI for transforming character images into animated videos controlled by desired pose sequences. Animating any character image into a video, unconstrained by specific domains 🚀
👉Review https://t.ly/qCahZ
👉Paper https://lnkd.in/d-zi8EZ6
👉Project https://lnkd.in/djwjQRvq
👉Code https://lnkd.in/dDMkjnKz
👑 HD Generative #AI With No $$$ 👑
👉DemoFusion: a novel approach for HD image generation w/ no money. Progressive Upscaling, Skip Residual, & Dilated Sampling to achieve higher-resolution ever 🔥
👉Review https://t.ly/sIqDV
👉Paper https://lnkd.in/deDt-zcK
👉Project https://lnkd.in/dFGj47Xw
👉Code https://lnkd.in/dY3UcXwp
🧱 Material Palette from Images 🧱
👉A novel problem in #AI: material extraction from a real-world image without any prior knowledge 🤯
👉Discussion https://t.ly/AIWs-
👉Paper https://lnkd.in/dBFAVWPF
👉Project https://lnkd.in/dV5jK8Sm
👉Code https://lnkd.in/dNhMnfFb
👉Dataset (coming) ...
🌳 NebulOS: (more than) Green AI 🌳
👉A novel hardware-aware Training-Free NAS approach that considers both training-free metrics & HW constraints, aiming to find the optimal balance between validation accuracy & energy consumption. 🚀
👉Review https://t.ly/Ozso1
👉Project sites.google.com/view/nebulos
👉Code https://github.com/fracapuano/NebulOS
👉Video https://lnkd.in/exN4Q2Fu
👉Hugging Face demo https://lnkd.in/eyCcPEPc
🎡 Panoptic Video Scene Graph 🎡
👉Combining video scene graph generation w/ panoptic segmentation for holistic video understanding. Novel HQ dataset with fine, temporal scene graph annotations & panoptic segmentation. Code released!🔥
👉Review https://t.ly/tckDT
👉Project jingkang50.github.io/PVSG/
👉Paper arxiv.org/pdf/2311.17058.pdf
👉Code github.com/LilyDaytoy/OpenPVSG
👉Tool github.com/lilyDaytoy/PVSGAnnotation
🔥 Stable (Stability.AI) Video Diffusion 🔥
👉 #StabilityAI released Stable Video Diffusion: latent video diffusion model for high-resolution, SOTA text-to-video and image-to-video generation
👉 Review https://t.ly/XwHys
👉 Code https://lnkd.in/dQw_yNuV
👉 Paper https://lnkd.in/dHn6f787
🦖T-Rex: Counting by Visual Prompting🦖
👉T-Rex: a novel interactive object counting model to detect and count any objects. Impressive results!
👉Review https://t.ly/4SfFX
👉Project https://lnkd.in/dVtEndHv
👉Paper https://lnkd.in/dBGQsbdP
👉Code (not announced, but an empty repo exists): https://lnkd.in/dnZnGRUn
🧿 Model-aware 3D Eye Gaze 🧿
👉 Novel hybrid approach that outputs 3D eye model, semantic segmentation, cam-intrinsic & pose. Only 2D eye semantic segmentation masks and fewer 3D gaze labels for supervision.
👉Review https://t.ly/AdKRf
👉Paper https://lnkd.in/dWb9GHPh
👉Code https://lnkd.in/dfAWFVky
🔳 SOTA Semantic Boundary 🔳
👉Mobile-Seed, a lightweight, dual-task framework tailored for simultaneous semantic segmentation and boundary detection.
👉Review https://t.ly/GsArZ
👉Project whu-usi3dv.github.io/Mobile-Seed/
👉Paper arxiv.org/pdf/2311.12651.pdf
👉Code github.com/WHU-USI3DV/Mobile-Seed
🍿 Segmenting anything in 3D 🍿
👉 OmniSeg3D: omniversal segmentation method aims for segmenting anything in 3D all at once.
👉Review https://t.ly/Q0jrK
👉Paper https://lnkd.in/d9qpxXY9
👉Code (soon)
🌦️ 100+ GPU weather training 🌦️
👉#NVIDIA just released Makani: massively parallel training of weather and climate prediction models on 100+ GPUs and to enable the development of the next generation of weather and climate models.
👉 Discussion https://lnkd.in/dMgakzWm
👉 Project & Code https://lnkd.in/d4NFZ5xi
🐓 Emu: image edit / video gen. 🐓
👉#Meta the new SOTA in text-to-video generation and instruction-based image editing.
👉 Review https://t.ly/PMTBc
👉 Paper (image edit): https://lnkd.in/eVadH-QS
👉 Project https://lnkd.in/eG8eWUJY
👉 Paper (video gen): https://lnkd.in/eVadH-QS
👉 Project https://lnkd.in/eu6Zu6gp
💥🚗 CrashCar101: Generative Damaged Cars💥🚗
👉 CrashCar101: procedural generation pipeline that damages 3D car models to obtain synthetic damaged cars paired with pixel-accurate annotations
👉 Review https://t.ly/pITHm
👉 Paper https://lnkd.in/dzp6q3T5
👉 Project https://lnkd.in/daRXg73N
🔥Florence-2: unified Computer Vision🔥
👉#Microsoft announces Florence-2: novel foundation model with unified, prompt-based, representation for a large variety of #computervision & vision-language task. One backbone -> multiple tasks!
👉Review https://t.ly/pOins
👉Paper arxiv.org/pdf/2311.06242.pdf
👉Project www.microsoft.com/en-us/research/project/projectflorence/
