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 7 509 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 25 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 261, 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 16.91%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.04% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 918 visualizaciones. En el primer día suele acumular 1 216 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 16.
- 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 26 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
+224 horas
+17 días
+26130 días
Archivo de publicaciones
🌵 G-Splatting Controllable Portraits 🌵
👉From monocular/casual video captures, Rig3DGS rigs 3D Gaussian Splatting to enable the creation of re-animatable portrait videos with control over facial expressions, head-pose and viewing direction. Authors: Stony Brook University & #Adobe
👉Review https://t.ly/fq71w
👉Paper https://arxiv.org/pdf/2402.03723.pdf
👉Project shahrukhathar.github.io/2024/02/05/Rig3DGS.html
🪵 HASSOD Object Detection 🪵
👉 HASSOD: fully self-supervised detection and instance segmentation. The new SOTA able to understand the part-to-whole object composition like humans do.
👉Review https://t.ly/66qHF
👉Paper arxiv.org/pdf/2402.03311.pdf
👉Project hassod-neurips23.github.io/
👉Repo github.com/Shengcao-Cao/HASSOD
💥 #Py4AI: 2x speakers, 2x tickets! 💥
✅Doubling the speakers (6 -> 12!)
✅Adding a new track (2 tracks in parallel)
✅Releasing a new batch of 100 tickets!
👉 More: https://t.ly/WmVrM
🏇Bootstrapping TAP 🏇
👉#Deepmind shows how large-scale, unlabeled, uncurated real-world data can improve TAP with minimal architectural changes, via a self-supervised student-teacher setup. Source Code released 💙
👉Review https://t.ly/-S_ZL
👉Paper https://arxiv.org/pdf/2402.00847.pdf
👉Code https://lnkd.in/gyi7Dhkn
🍬 ABS: SOTA collision-free 🍬
👉ABS (Agile But Safe): learning-based control framework for agile and collision-free locomotion for quadrupedal robot. Source Code announced (coming) 💙
👉Review https://t.ly/AYu-Z
👉Paper arxiv.org/pdf/2401.17583.pdf
👉Project agile-but-safe.github.io/
👉Repo github.com/LeCAR-Lab/ABS
🚦(adding) Anything in Any Video🚦🚦
👉 XPeng Motors announced Anything in Any Scene: novel #AI for realistic video simulation that seamlessly inserts any object into an existing dynamic video. Strong emphasis on realism, the objects in the BBs don't exist. Source Code released 💙
👉Review https://t.ly/UYhl0
👉Code https://lnkd.in/gyi7Dhkn
👉Paper https://lnkd.in/gXyAJ6GZ
👉Project https://lnkd.in/gVA5vduD
🚦(adding) Anything in Any Video🚦🚦
👉 XPeng Motors announced Anything in Any Scene: novel #AI for realistic video simulation that seamlessly inserts any object into an existing dynamic video. Strong emphasis on realism, the objects in the BBs don't exist. Source Code released 💙
👉Review https://t.ly/UYhl0
👉Code https://lnkd.in/gyi7Dhkn
👉Paper https://lnkd.in/gXyAJ6GZ
👉Project https://lnkd.in/gVA5vduD
🎉 ADΔER: Event-Camera Suite 🎉
👉ADΔER: a novel/unified framework for event-based video. Encoder / transcoder / decoder for ADΔER (Address, Decimation, Δt Event Representation) video streams. Source code (RUST) released 💙 H/T author: Andrew C. Freeman from University of North Carolina, USA.
👉Review https://t.ly/w5_KC
👉Paper arxiv.org/pdf/2401.17151.pdf
👉Repo github.com/ac-freeman/adder-codec-rs
↘️ SEELE: "moving" the subjects ➡️
👉Subject repositioning: manipulating an input image to reposition one of its subjects to a desired location while preserving the image’s fidelity. SEELE is a single diffusion model to address this novel generative sub-tasks
👉Review https://t.ly/4FS4H
👉Paper https://arxiv.org/pdf/2401.16861.pdf
👉Project https://yikai-wang.github.io/seele/
🥓 RANSAC -> PARSAC (neural) 🥓
👉Neural PARSAC: estimating multiple vanishing points (V), fundamental matrices (F) or homographies (H) at the speed of light! Source Code released 💙
👉Review https://t.ly/r9ngg
👉Paper https://lnkd.in/dadQ4Qec
👉Code https://lnkd.in/dYp6gADd
🍋 Diffutoon: new SOTA video 🍋
👉Diffutoon is a cartoon shading approach, aiming to transform photorealistic videos in anime styles. It can handle exceptionally high resolutions and rapid motions. Source code released!
👉Review https://t.ly/sim2O
👉Paper https://lnkd.in/dPcSnAUu
👉Code https://lnkd.in/d9B_dGrf
👉Project https://lnkd.in/dpcsJcX2
🧠350+ Free #AI Courses by #Google🧠
👉350+ free courses from #Google to become professional in #AI & #Cloud. The full catalog (900+) includes a variety of activity: videos, documents, labs, coding, and quizzes. 15+ supported languages. No excuse.
✅𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈
✅𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐋𝐌𝐬
✅𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐓𝐅
✅𝐁𝐚𝐬𝐞𝐥𝐢𝐧𝐞 𝐃𝐚𝐭𝐚, 𝐌𝐋, 𝐀𝐈
✅𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈
👉Review: https://t.ly/517Dr
👉Full list: https://www.cloudskillsboost.google/catalog?page=1
🐙 Rock-Track announced! 🐙
👉Key Lab of Robotics and System announced Rock-Track, the evolution of Poly-MOT, the previous SOTA in 3D MOT Tracking-By-Detection framework.
👉Review https://t.ly/hC0ak
👉Repo, coming: https://lnkd.in/dtDkPwCC
👉Paper coming
🌋EasyVolcap: Accelerating Neural Volumetric🌋
👉Novel #PyTorch library for accelerating neural volumetric video research: volumetric video capturing, reconstruction & rendering
👉Review https://t.ly/8BISl
👉Paper arxiv.org/pdf/2312.06575.pdf
👉Code github.com/zju3dv/EasyVolcap
🦩 WildRGB-D: Objects in the Wild 🦩
👉#NVIDIA unveils a novel RGB-D object dataset captured in the wild: ~8500 recorded objects, ~20,000 RGBD videos, 46 categories with corresponding masks and 3D point clouds.
👉Review https://t.ly/WCqVz
👉Data github.com/wildrgbd/wildrgbd
👉Paper arxiv.org/pdf/2401.12592.pdf
👉Project wildrgbd.github.io/
👢"Virtual Try-All" by #Amazon 👢
👉#Amazon announces ”Diffuse to Choose”: diffusion-based image-conditioned inpainting for VTON. Virtually place any e-commerce item in any setting.
👉Review https://t.ly/at07Y
👉Paper https://lnkd.in/dxR7nGtd
👉Project diffuse2choose.github.io/
🫧 SAM + Open-Models 🫧
👉Grounded SAM (w/ DINO) as an open-set detector to combine with SAM. This integration enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models. It can seamlessly integrate with other Open-World models to accomplish more intricate visual tasks.
👉Review https://t.ly/FwasQ
👉Paper https://arxiv.org/pdf/2401.14159.pdf
👉Code https://github.com/IDEA-Research/Grounded-Segment-Anything
🧪 SUPIR: SOTA restoration 🧪
👉SUPIR is the new SOTA in image restoration; suitable for restoration of blurry objects, defining the material texture of objects, and adjusting restoration based on high-level semantics
👉Review https://t.ly/wgObH
👉Project https://lnkd.in/dPcnBKWm
👉Paper https://lnkd.in/dZPYcUuq
👉Demo coming 🩷 but no code announced :(
🔥Lumiere: SOTA video-gen🔥
👉#Google unveils Lumiere: Space-Time Diffusion Model for Realistic Video Generation. It's the new SOTA, tasks: Text-to-Video, Video Stylization, Cinemagraphs & Video Inpainting.
👉Review https://t.ly/nalJR
👉Paper https://lnkd.in/d-PvrGjT
👉Project https://t.ly/gK8hz
🎭 UltrAvatar: ULTRA-Realistic Avatar 🎭
👉Novel 3D avatar with enhanced fidelity of geometry, and superior quality of physically based rendering (PBR) textures without unwanted lighting.
👉Review https://t.ly/B3BEu
👉Project https://lnkd.in/dkUQHFEV
👉Paper https://lnkd.in/dtEQxrBu
👉Code coming 🩷
