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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
⚽ Dynamic NeRFs for Soccer ⚽
👉SoccerNeRF: first attempt of "cheap" NeRF applied to football for reconstructing soccer replays in space and time.
😎Review https://t.ly/Ywcvk
😎Paper arxiv.org/pdf/2309.06802.pdf
😎Project https://soccernerfs.isach.be/
😎Code github.com/iSach/SoccerNeRFs
🦊 MagiCapture: HD Multi-Concept Portrait 🦊
👉KAIST unveils MagiCapture: integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references
😎Review https://t.ly/c9rOo
😎Paper https://arxiv.org/pdf/2309.06895.pdf
🧄FreeMan: towards #3D Humans 🧄
👉FreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!
😎Review https://t.ly/ICxpA
😎Paper arxiv.org/pdf/2309.05073.pdf
😎Project wangjiongw.github.io/freeman
🔥🔥 #META's DINOv2 is now commercial! 🔥🔥
👉Universal features for image classification, instance retrieval, video understanding, depth & semantic segmentation. Now suitable for commercial.
😎Review https://t.ly/LNrGy
😎Paper arxiv.org/pdf/2304.07193.pdf
😎Code github.com/facebookresearch/dinov2
😎Demo https://dinov2.metademolab.com/
🪷 Diffusive Consistent Video Editing 🪷
👉 Weizmann Institute of Science unveils TokenFlow, a novel text-to-image diffusion model for text-driven video editing
😎Review https://t.ly/ru8km
😎Paper arxiv.org/pdf/2307.10373.pdf
😎Project diffusion-tokenflow.github.io
😎Code github.com/omerbt/TokenFlow
🍃 Tracking Anything with Decoupled VOS 🍃
👉A novel VOS approach that extends Segment Anything (SAM) to video for open-world video segmentation with no user input required
😎Review https://t.ly/xeobR
😎Paper arxiv.org/pdf/2309.03903.pdf
😎Project hkchengrex.com/Tracking-Anything-with-DEVA
😎Code github.com/hkchengrex/Tracking-Anything-with-DEVA
😎Colab https://colab.research.google.com/drive/1OsyNVoV_7ETD1zIE8UWxL3NXxu12m_YZ
♊️ Doppelgangers in Structures ♊️
👉A novel learning-based approach to visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions
😎Review https://t.ly/9yLot
😎Paper arxiv.org/pdf/2309.02420.pdf
😎Code github.com/RuojinCai/Doppelgangers
😎Project doppelgangers-3d.github.io/
⛺FACET: Fairness in Computer Vision⛺
👉#META AI opens a large, publicly available dataset for classification, detection & segmentation. Potential performance disparities & challenges across sensitive demographic attributes
😎Review https://t.ly/mKn-t
😎Paper arxiv.org/pdf/2309.00035.pdf
😎Dataset https://facet.metademolab.com/
🎍RoboTAP: Dense Tracking for Few-Shot Imitation🎍
👉RoboTAP is a novel dense tracking representation for robotic arm.
😎Review https://t.ly/MCO_V
😎Paper arxiv.org/pdf/2308.15975.pdf
😎Project https://robotap.github.io/
😎Code github.com/deepmind/tapnet
🐦 3D Pigeons Pose and Tracking 🐦
👉 3D-MuPPET: estimate and track 3D poses of pigeons with multiple-views
😎Review https://t.ly/jfAJJ
😎Paper arxiv.org/pdf/2308.15316.pdf
😎Code github.com/alexhang212/3D-MuPPET/
✂️ VideoCutLER: Super Simple UVIS ✂️
👉VideoCutLER is a simple unsupervised video instance segmentation (UVIS) method without relying on optical flows
😎Review https://t.ly/PBBjG
😎Paper arxiv.org/pdf/2308.14710.pdf
😎Project people.eecs.berkeley.edu/~xdwang/projects/CutLER
😎Code github.com/facebookresearch/CutLER/tree/main/videocutler
🌲 MagicEdit: Magic Video Editing 🌲
👉MagicEdit: explicit disentangling the learning of content, structure & motion for Hi-Fi and temporally coherent video editing.
😎Report https://t.ly/tREX4
😎Paper https://arxiv.org/pdf/2308.14749.pdf
😎Project https://magic-edit.github.io/
😎Code github.com/magic-research/magic-edit
🌲 MagicEdit: Magic Video Editing 🌲
👉MagicEdit: explicit disentangling the learning of content, structure & motion for Hi-Fi and temporally coherent video editing.
😎Report https://t.ly/tREX4
😎Paper https://arxiv.org/pdf/2308.14749.pdf
😎Project https://magic-edit.github.io/
😎Code github.com/magic-research/magic-edit
🪶 ReST: Multi-Camera MOT 🪶
👉Novel reconfigurable two-steps graph model for multi-camera multi object video tracking (MC-MOT)
😎Review https://t.ly/3C5tb
😎Paper arxiv.org/pdf/2308.13229.pdf
😎Code github.com/chengche6230/ReST
💡 Relighting NeRF 💡
👉Neural implicit radiance representation for free viewpoint relighting of an object lit by a moving point light
😎Review https://t.ly/J-3_L
😎Project nrhints.github.io
😎Code github.com/iamNCJ/NRHints
😎Paper nrhints.github.io/pdfs/nrhints-sig23.pdf
🐨 Watch Your Steps: Editing by Text 🐨
👉The novel SOTA in image & scene (text) editing via denoising diffusion models
😎Review https://t.ly/fv9wn
😎Paper arxiv.org/pdf/2308.08947.pdf
😎Project ashmrz.github.io/WatchYourSteps
🥕 Scenimefy: I-2-I for anime 🥕
👉S-Lab unveils a novel semi-supervised I-2-I translation framework + HD dataset for anime
😎Review https://t.ly/IsdEG
😎Paper arxiv.org/pdf/2308.12968.pdf
😎Code https://github.com/Yuxinn-J/Scenimefy
😎Project https://yuxinn-j.github.io/projects/Scenimefy.html
🌆 NeO360: NeRF for Sparse Outdoor 🌆
👉#Toyota (+GIT) unveils NeO360: 360◦ outdoor scenes from a single or a few posed RGB images
😎Review https://t.ly/JDJZg
😎Paper arxiv.org/pdf/2308.12967.pdf
😎Project zubair-irshad.github.io/projects/neo360.html
🌵 POCO: 3D HPS using Confidence 🌵
👉 Novel framework for HPS regression: #3D human body + confidence in a single feed-forward pass
😎Review https://t.ly/cDePe
😎Paper arxiv.org/pdf/2308.12965.pdf
😎Project https://poco.is.tue.mpg.de
