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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 17 142 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 17 142 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.
17 142
Suscriptores
-224 horas
-367 días
-19030 días
Archivo de publicaciones
👗👗 AG3D: SOTA #3D clothed avatars from 2D👗👗
👉The novel SOTA in adversarial generative model of realistic 3D people is out.
😎Review https://t.ly/vnJO7
😎Paper zj-dong.github.io/AG3D/assets/paper.pdf
😎Project https://zj-dong.github.io/AG3D
😎Code https://github.com/zj-dong/AG3D
🦹♀️ Snap's Hyper-Realistic Human 🦹♀️
👉New diffusive #AI by Snap that generates in-the-wild human images with hyper-realism. Swipe the gallery, NUTS!👇
😎Gallery https://t.ly/cG74X
😎Paper arxiv.org/pdf/2310.08579.pdf
😎Project snap-research.github.io/HyperHuman
😎Code github.com/snap-research/HyperHuman
🙋 Full Human Motion 🙋
👉OmniControl by Google is novel framework for text-conditioned human motion generation model based on diffusion process
😎Review https://t.ly/F_0Ov
😎Paper arxiv.org/pdf/2310.08580.pdf
😎Project neu-vi.github.io/omnicontrol/
📊 TextPSG: PSG from Text 📊
👉A novel problem in #AI: Panoptic Scene Graph Generation from Purely Textual Descriptions (Caption-toPSG)
😎Review https://t.ly/UXEmk
😎Paper arxiv.org/pdf/2310.07056.pdf
😎Project vis-www.cs.umass.edu/TextPSG
😎Code github.com/chengyzhao/TextPSG
🏊 SwimXYZ: Synthetic Swimming 🏊
👉SwimXYZ: synthetic dataset for swimming, monocular videos annotated with ground truth 2D and 3D joints
💚💙 Where Is OpenCV 5? 💙💚
👉On October 24th, the organization is launching a crowdfunding campaign to raise funds for #OpenCV 5 development.
👆me in 2005 during my thesis work about face tracking; up to 50x faster than the previous SOTA. No chance to did it without OpenCV library and support from the community.
🔥Support #OpenCV 5 to create the next-gen of researchers and scientists. More: https://t.ly/UTukV
🔥Visual-Math Q&A: MathVista is out! 🔥
👉 MathVista is the ultimate benchmark designed to amalgamate challenges from diverse mathematical and visual tasks
😎Review https://t.ly/yfqHZ
😎Paper https://arxiv.org/pdf/2310.02255.pdf
😎Project https://mathvista.github.io/
😎Code github.com/lupantech/MathVista
🌱 Making LLaMA See and Draw 🌱
👉Tencent #AI planted a SEED of Vision in Large Language Model. Making LLaMA see 'n' draw stuff.
😎Review https://t.ly/QiCAv
😎Paper arxiv.org/pdf/2310.01218.pdf
😎Code github.com/AILab-CVC/SEED
☕Decaf: 3D Face-Hand Interactions☕
👉The first learning-based MoCap to track human hands interacting with human faces in #3D from single monocular RGB videos
😎Review https://t.ly/070Tj
😎Paper arxiv.org/pdf/2309.16670.pdf
😎Project vcai.mpi-inf.mpg.de/projects/Decaf
🧱 Generating Scenes from Touch 🧱
👉#AI for synthesizing images from tactile signals (and vice versa) and apply it to a number of visuo-tactile synthesis tasks
😎Review https://t.ly/Gxr0L
😎Paper https://arxiv.org/pdf/2309.15117.pdf
😎Project https://fredfyyang.github.io/vision-from-touch
😎Code https://github.com/fredfyyang/vision-from-touch
🌮 OW Indoor Segmentation 🌮
👉3D-OWIS is a novel open-world 3D indoor instance segmentation method (with auto-labeling scheme) to separate known/unknown category labels
😎Review https://t.ly/-7ALf
😎Paper arxiv.org/pdf/2309.14338.pdf
😎Code github.com/aminebdj/3D-OWIS
🌬️ Neural Blowing in Still Photos 🌬️
👉 A novel approach to animate human hair (and clothes) in a still portraits
😎Review https://t.ly/HKG0t
😎Paper arxiv.org/pdf/2309.14207.pdf
😎Project nevergiveu.github.io/AutomaticHairBlowing
😎Paper https://arxiv.org/pdf/2309.14207.pdf
😎Project https://nevergiveu.github.io/AutomaticHairBlowing
🛵CoTracker: fast transformer-tracker🛵
👉META's CoTracker is a fast transformer-based model that can track any point in a video
😎Review https://t.ly/M36A_
😎Paper arxiv.org/pdf/2307.07635.pdf
😎Project https://co-tracker.github.io/
😎Code github.com/facebookresearch/co-tracker
🍟 DE-ViT: detecting everything via DINOv2 🍟
👉DE-ViT: open-set object detector based on DINOv2 backbone. It's the new SOTA on COCO & LVIS dataset
😎Review https://t.ly/_DAmt
😎Paper arxiv.org/pdf/2309.12969.pdf
😎Code https://github.com/mlzxy/devit
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ https://t.me/DataScienceM
🫀CPR-Coach: Neural Cardiopulmonary Resuscitation🫀
👉CPR-Coach: fine-grained action recognition in cardiopulmonary resuscitation
😎Review https://t.ly/Qbg4K
😎Paper arxiv.org/pdf/2309.11718.pdf
😎Code github.com/Shunli-Wang/CPR-Coach
😎Project shunli-wang.github.io/CPR-Coach
This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/DataScienceM
☢️ GlueStick: Graph Neural Matching ☢️
👉GlueStick is joint deep matcher for points and lines that leverages the connectivity information between nodes to better glue them together
😎Review https://t.ly/Atxqo
😎Paper arxiv.org/pdf/2304.02008.pdf
😎Code https://github.com/cvg/GlueStick
