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 17 259 suscriptores, ocupando la posición 7 497 en la categoría Tecnologías y Aplicaciones y el puesto 2 130 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 259 suscriptores.
Según los últimos datos del 21 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 260, y en las últimas 24 horas de 6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 20.25%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.12% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 496 visualizaciones. En el primer día suele acumular 1 229 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 25.
- 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 22 octubre, 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.
17 259
Suscriptores
+624 horas
+97 días
+26030 días
Archivo de publicaciones
🍵3D from Single Human RGB🍵
👉Real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations. Code TBA💙
👉Review https://lnkd.in/p/dt-B2QMt
👉Paper https://arxiv.org/pdf/2610.28880
👉Project https://real2sem2real.github.io/
👉Repo TBA
🥭Agentic Manga->Animation🥭
👉Manga2Animate: turning existing manga pages into coherent animation that stays faithful to the original manga. Impressive results, best with sound. Code released.💙
👉Review https://lnkd.in/p/dHpdPAj3
👉Paper https://arxiv.org/pdf/2610.26851
👉Project https://annchenn.github.io/Manga2Animate/
👉Repo https://github.com/annchenn/Manga2Animate
🔥SOTA Video Matting (long clips)🔥
👉VideoMaMa++ (#NeuroIPS 2026): mask-guided video matting framework that recovers fine-grained details (e.g., hair strands) and scales reliably to long videos. Impressive results, NC code announced💙
👉Review https://lnkd.in/p/dj3_zE3P
👉Paper https://arxiv.org/pdf/2610.26857
👉Project https://cvlab-kaist.github.io/VideoMaMa-PlusPlus/
👉Repo https://github.com/cvlab-kaist/VideoMaMa-PlusPlus
🏵️Lang-Informed Lossless Encoder🏵️
👉Stanford's LILIE: an invertible image transform trained such that a vision-language model can recover captions from noised features. Code, encoders, and generative models💙
👉Review https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2610.23653
👉Project https://www.suchiragarwal.com/lilie/
👉Repo https://github.com/SuchirAgarwal/lilie
🔥700 FPS Humans Recovery🔥
👉One model, any sparse signal: OmniPoser recovers full-body 3D human motion from 3D keypoints, sparse IMU rotations, head-mounted 6-DoF poses, or any subset or combination of these signals. All within a SINGLE model. Repo under nc-cc license💙
👉Review https://lnkd.in/p/e-xAquHt
👉Paper https://arxiv.org/pdf/2610.19857
👉Project https://jamesbridgewater51.github.io/omniposer-eccv/
👉Repo https://github.com/JamesBridgewater51/omniposer
Back to post on Twitter 😈
https://x.com/ai_withpapers
🎊 Pixel-Level 4D Humans 🎊
👉Dense4D from the Imperial College is the first feed-forward model dedicated to dense 4D correspondence across the body, clothing and hair from a set of monocular RGB images, without a body template. Code announced💙
👉Review https://lnkd.in/p/e9GXGggz
👉Paper https://arxiv.org/pdf/2610.22325
👉Project https://alpguler.com/dense4d/
👉Repo TBA
🧊SOTA category-level 9D Pose🧊
👉SJTU at #NeurIPS 2026 with the new SOTA among RGB-based methods for category-level 9D pose estimation. Code announced💙
👉Review https://lnkd.in/p/e5BvM7Ua
👉Paper https://arxiv.org/pdf/2610.18374
👉Project https://souglad21.github.io/SchemaPose/
👉Repo TBA
🐇Segment where you look🐇
👉KAUST (+Adobe) revisited the interface through which a vision-language model passes the location of its referent to a segmentation decoder. Code announced💙
👉Review https://lnkd.in/p/er_ifxGR
👉Paper https://lnkd.in/eATXw98R
👉Project https://lnkd.in/eXSWvxPN
👉Repo TBA
Hi everyone 👋
over the past months, engagement on my posts has grown a lot, which tells me the content you value is getting better. At the same time, impressions have dropped, and I think a big part of the reason is the flood of low-quality viral posts filling many of our feeds. PROVE ME WRONG 😂
The best way to push back is to engage with the posts that are actually worth your time. Open the post on LinkedIn, then leave a comment (even a couple of lines with your take), and like, share, or save it if it's useful. Comments are the most valuable, but every action helps. With ~100,000 of us following along, this is how we tell the algorithm, and me, what deserves more space.
Fight fire with fire!
🥬 Rotational neural keypoint 🥬
👉RaCo is a rotationally equivariant neural keypoint detector that identifies stable and repeatable keypoints. Code under Apache 2.0💙
👉Review https://lnkd.in/p/ezQzUwju
👉Paper https://arxiv.org/pdf/2602.15755
👉Repo https://github.com/cvg/RaCo
🫏What EGO does not teach🫏
👉A brilliant work about a controlled study of how interaction fidelity in ego-centric world models behaves as a function of scale. Closing this gap will depend on how models are trained, not only on how much data they see. Repo available soon💙
👉Review https://lnkd.in/p/dzKbCCfr
👉Paper https://lnkd.in/dcpReY9E
👉Project https://lnkd.in/dnpfpZnG
👉Repo TBA
🟨 Rome from ONE pic 🟥
👉Detailed scene meshes from one photograph: the method completes geometry beyond the observed view and supports indoor, outdoor, and large-scale scenes. Repo announced💙
👉Review https://lnkd.in/p/eMdWBEGn
👉Paper https://arxiv.org/pdf/2610.08790
👉Project https://build-rome.github.io/
👉Repo TBA
🐇Physically Plausible 3D Motion🐇
👉Physically plausible motion recovery: given a monocular video, FlowHMR recovers global 3D human motion that a physics-based controller can successfully track in simulation. Repo💙
👉Review https://lnkd.in/p/d77fzUtR
👉Paper https://arxiv.org/pdf/2610.03691
👉Project https://flowhmr.github.io/
👉Repo https://github.com/flowhmr/flowhmr
🔥The Computer Vision ultimate collection🔥
👉Stan Birchfield (#Nvidia) just dropped this on arXiv. From classical image processing and 3D geometry to CNNs, Transformers, foundation models, and neural rendering. What makes this book damn good is the combination of clear explanations and working Python. A gift.
👉Review https://lnkd.in/p/ejwm_DVn
👉Book https://lnkd.in/eTrEvmd9
👉Code https://lnkd.in/eakj9VZU
🔥🔥 70,000+ 🔥🔥
👉 Crazy how a boring science project (no kittens, no rants, no personal dramas) can reach for 70,000+ followers. Speechless.
Love u 💛
👉 https://lnkd.in/p/eD6Xxdxi
🔥Ego-Exo4D Human Dataset🔥
👉Form the University of Austin, Ego-Exo4D-HM: large-scale dataset of 4D human motion reconstructions for Ego-Exo4D’s captures + reconstruction pipeline. Code, dataset, and docs released💙
👉Review https://lnkd.in/p/eVFt9jPr
👉Paper https://lnkd.in/eWj4cD7T
👉Project https://lnkd.in/euPqVNxV
🔥TrackEverything is out🔥
👉TrackEverything is the first 3D point tracker capable of tracking all visible points across long horizons (1000+ frames). Repo announced💙
👉Review https://lnkd.in/p/eCPJ6h2B
👉Paper https://arxiv.org/pdf/2609.30222
👉Project https://trackeverything.github.io/
👉Repo https://github.com/ayushjain1144/trackeverything
🦴3D Foundational Radiology🦴
👉nnFoundation: 3D radiological foundation models designed for transferable representation learning across heterogeneous tasks/datasets. Models released💙
👉Review https://lnkd.in/p/ekv-TSN8
👉Paper https://arxiv.org/pdf/2609.26924
👉Models https://huggingface.co/collections/MIC-DKFZ/nnfoundation
🩻Universal X-ray Segmentation🩻
👉FleXray: universal anatomical segmentation across the entire body in clinical X-rays. Built on a scalable, physics-based generative X-ray data engine. Repo under MIT💙
👉Review https://lnkd.in/p/e9MUk_eq
👉Paper https://arxiv.org/pdf/2609.26756
👉Project https://flexray.csail.mit.edu/
👉Repo https://github.com/VictorButoi/FleXray
