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 17 261 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 17 261 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.
17 261
Suscriptores
+224 horas
+17 días
+26130 días
Archivo de publicaciones
🐻StableNormal: Stable/Sharp Normal🐻
👉Alibaba unveils StableNormal, a novel method which tailors the diffusion priors for monocular normal estimation. Hugging Face demo is available💙
👉Review https://t.ly/FPJlG
👉Paper https://arxiv.org/pdf/2406.16864
👉Demo https://huggingface.co/Stable-X
🧬 Event-driven SuperResolution 🧬
👉USTC unveils EvTexture, the first VSR method that utilizes event signals for texture enhancement. It leverages high-frequency details of events to better recover texture regions in VSR. Source Code available💙
👉Review https://t.ly/zlb4c
👉Paper arxiv.org/pdf/2406.13457
👉Code github.com/DachunKai/EvTexture
🤓 Glasses-Removal from Videos🤓
👉Lightricks unveils a novel method able to receive an input video of a person wearing glasses, and consistently removes the glasses, while preserving the ID. It works even when there are reflections, heavy makeup, and eye blinks. Code announced, not yet released💙
👉Review https://t.ly/Hgs2d
👉Paper arxiv.org/pdf/2406.14510
👉Project https://v-lasik.github.io/
👉Code github.com/v-lasik/v-lasik-code
🌱 TokenHMR : new 3D human pose SOTA 🌱
👉TokenHMR is the new SOTA HPS method mixing 2D keypoints and 3D pose accuracy, thus leveraging Internet data without known camera parameters. It's the new SOTA by a large margin.
👉Review https://t.ly/K9_8n
👉Paper arxiv.org/pdf/2404.16752
👉Project tokenhmr.is.tue.mpg.de/
👉Code github.com/saidwivedi/TokenHMR
💦 Self-driving in wet conditions 💦
👉BMW SemanticSpray: novel dataset contains scenes in wet surface conditions captured by camera, LiDAR and radar. Camera: 2D Boxes | LiDAR: 3D Boxes, Semantic Labels | Radar: Semantic Labels.
👉Review https://t.ly/8S93j
👉Paper https://lnkd.in/dnN5MCZC
👉Project https://lnkd.in/dkUaxyEF
👉Data https://lnkd.in/ddhkyXv8
🧤HOT3D Hand/Object Tracking🧤
👉#Meta opens a novel egocentric dataset for 3D hand & object tracking. A new benchmark for vision-based understanding of 3D hand-object interactions. Dataset available 💙
👉Review https://t.ly/cD76F
👉Paper https://lnkd.in/e6_7UNny
👉Data https://lnkd.in/e6P-sQFK
🌵 RobustSAM for Degraded Images 🌵
👉RobustSAM, the evolution of SAM for degraded images; enhancing the SAM’s performance on low-quality images while preserving prompt-ability & zeroshot generalization. Dataset & Source Code released💙
👉Review https://t.ly/mnyyG
👉Paper arxiv.org/pdf/2406.09627
👉Project robustsam.github.io
👉Code github.com/robustsam/RobustSAM
📫 MeshPose: DensePose + HMR 📫
👉MeshPose: novel approach to jointly tackle DensePose and Human Mesh Reconstruction in a while. A natural fit for #AR applications requiring real-time mobile inference.
👉Review https://t.ly/a-5uN
👉Paper https://arxiv.org/pdf/2406.10180
👉Project https://meshpose.github.io/
🎹 PianoMotion10M for gen-hands 🎹
👉PianoMotion10M: 116 hours of piano playing videos from a bird’s-eye view with 10M+ annotated hand poses. A big contributions in hand motion generation. Code & Dataset released💙
👉Review https://t.ly/_pKKz
👉Paper arxiv.org/pdf/2406.09326
👉Code https://lnkd.in/dcBP6nvm
👉Project https://lnkd.in/d_YqZk8x
👉Dataset https://lnkd.in/dUPyfNDA
🍉 MASA: MOT Anything By SAM 🍉
👉MASA: Matching Anything by Segmenting Anything pipeline to learn object-level associations from unlabeled images of any domain. An universal instance appearance model for matching any objects in any domain. Source code in June 💙
👉Review https://t.ly/pKdEV
👉Paper https://lnkd.in/dnjuT7xm
👉Project https://lnkd.in/dYbWzG4E
👉Code https://lnkd.in/dr5BJCXm
👑 Kling AI vs. OpenAI Sora 👑
👉Kling: the ultimate Chinese text-to-video model - rival to #OpenAI’s Sora. No papers or tech info to check, but stunning results from the official site.
👉Review https://t.ly/870DQ
👉Paper ???
👉Project https://kling.kuaishou.com/
👗 SOTA Multi-Garment VTOn Editing 👗
👉#Google (+UWA) unveils M&M VTO, novel mix 'n' match virtual try-on that takes as input multiple garment images, text description for garment layout and an image of a person. It's the new SOTA both qualitatively and quantitatively. Impressive results!
👉Review https://t.ly/66mLN
👉Paper arxiv.org/pdf/2406.04542
👉Project https://mmvto.github.io
🧊 Universal 6D Pose/Tracking 🧊
👉Omni6DPose is a novel dataset for 6D Object Pose with 1.5M+ annotations. Extra: GenPose++, the novel SOTA in category-level 6D estimation/tracking thanks to two pivotal improvements.
👉Review https://t.ly/Ywgl1
👉Paper arxiv.org/pdf/2406.04316
👉Project https://lnkd.in/dHBvenhX
👉Lib https://lnkd.in/d8Yc-KFh
🚙 UA-Track: Uncertainty-Aware MOT🚙
👉UA-Track: novel Uncertainty-Aware 3D MOT framework which tackles the uncertainty problem from multiple aspects. Code announced, not released yet.
👉Review https://t.ly/RmVSV
👉Paper https://arxiv.org/pdf/2406.02147
👉Project https://liautoad.github.io/ua-track-website
📞FacET: VideoCall Change Your Expression📞
👉Columbia University unveils FacET: discovering behavioral differences between conversing face-to-face (F2F) and on video-calls (VCs).
👉Review https://t.ly/qsQmt
👉Paper arxiv.org/pdf/2406.00955
👉Project facet.cs.columbia.edu/
👉Repo (empty) github.com/stellargo/facet
👹👹 AI and the Everything in the Whole Wide World Benchmark 👹👹
👉Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”.
👉Review https://t.ly/isdxM
👉Paper https://lnkd.in/dFraieZS
👉News https://lnkd.in/da-7PnVT
👹👹👹👹
Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”.
🐳 MultiPly: in-the-wild Multi-People 🐳
👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙
👉Review https://t.ly/_xjk_
👉Paper arxiv.org/pdf/2406.01595
👉Project eth-ait.github.io/MultiPly
👉Repo github.com/eth-ait/MultiPly
🐳MultiPly: in-the-wild Multi-People from Mono🐳
👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2406.01595
👉Project https://eth-ait.github.io/MultiPly/
👉Repo https://github.com/eth-ait/MultiPly
🎭New 2D Landmarks SOTA🎭
👉Flawless AI unveils FaceLift, a novel semi-supervised approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment, with no need for 3D landmark datasets. No code announced🥹
👉Review https://t.ly/lew9a
👉Paper arxiv.org/pdf/2405.19646
👉Project davidcferman.github.io/FaceLift
