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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
Mostrar más📈 Análisis del canal de Telegram AI with Papers - Artificial Intelligence & Deep Learning
El canal AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 17 147 suscriptores, ocupando la posición 7 723 en la categoría Tecnologías y Aplicaciones y el puesto 2 241 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 147 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -190, y en las últimas 24 horas de -2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 25.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 6.86% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 4 302 visualizaciones. En el primer día suele acumular 1 177 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 26.
- Intereses temáticos: El contenido se centra en temas clave como framework, object, dataset, tba, depth.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
17 147
Suscriptores
-224 horas
-367 días
-19030 días
Archivo de publicaciones
🔥 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/
