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
🖲️ Amodal Tracking Any Object 🖲️
👉Amodal tracking": inferring complete object boundaries, even when certain portions are occluded. New benchmark & approach, 2x better than SOTA in people tracking 🔥
👉Review https://t.ly/Rc6Ku
👉Paper https://lnkd.in/d39rFYT4
👉Project https://lnkd.in/d7bkEcni
👉(empty) Repo https://lnkd.in/dTsNKdfz
🧊 ControlNet for Depth Conditioning 🧊
👉KAUST (+ #Adobe) unveils LooseControl to control the generative image modeling process. Layout by boundaries and #3D box control via object locations (approximate bounding boxes). A step forward in generative design of the products.
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Review https://t.ly/9y72m
👉Paper https://arxiv.org/pdf/2312.03079.pdf
👉Project https://shariqfarooq123.github.io/loose-control/
👉Demo🤗 https://huggingface.co/spaces/shariqfarooq/LooseControl
👉Repo https://github.com/shariqfarooq123/LooseControl
🔥 #AIwithPapers: we are 8k+ 🔥
👉 After flirting with #ChatGpt for months, you back in love with this channel and #ComputerVision. I felt bad but I forgive you 🧡
😈 What about boosting us channel? Click: https://t.me/AI_DeepLearning?boost
😈Invite -> https://t.me/AI_DeepLearning
💃Outfit Anyone: Ultra-HQ VTO💃
👉Alibaba unveils Outfit Anyone: a two-stream conditional diffusion able to adeptly handle garment deformation for more lifelike results in VOT. Extra: Outfit Anyone + Animate Anyone for outfit + motion generation of any character. NO CODE / NO PAPER / DEMO AVAILABLE :)
👉Review https://t.ly/o6UR9
👉Demo https://lnkd.in/dpQYdXhc
👉Repo (empty) https://lnkd.in/dBsNST6r
🪼 PatchFusion: SOTA Mono-Depth 🪼
👉PatchFusion: novel end-to-end tile-based framework for hi-res monocular metric depth estimation. It's the new SOTA in metric depth estimation from mono. Code & Demo on Hugging Face able 🔥
👉Review https://t.ly/hv3yT
👉Paper https://lnkd.in/d9dXP7iP
👉Project https://lnkd.in/dQcvVJSx
👉Repo https://lnkd.in/dW2GdVR5
👉Demo https://lnkd.in/dFW-gAiY
📲 EdgeSAM: On-Device 40x SAM 📲
👉A novel hyper-optimized version of SAM for mobile devices such as #Iphone. Pure CNNs backbone (better suitable for ANE), up to 40x faster!
👉Review https://t.ly/m_vLH
👉Paper https://lnkd.in/gHZVZN2x
👉Project https://lnkd.in/gK8qEK8p
👉Repo https://lnkd.in/gj6YAGNv
👉Hugging Face https://lnkd.in/gUUHJvxz
🪩 DreaMoving: Human Generation 🪩
👉Alibaba strikes again with DreaMoving: a diffusion-based controllable video generation framework to produce HQ customized human videos.
👉Review https://t.ly/BD_Yf
👉Paper https://lnkd.in/gepP6Rjw
👉Project https://lnkd.in/gwm72cfS
👉Repo (empty) https://lnkd.in/gsc2Qt-F
🫶 #3D Hands with Transformers 🫶
👉 HaMeR is a robust and accurate Hand Mesh Recovery from images and video frames, based on Transformer architecture. It's the new SOTA.
👉Review https://t.ly/YtAW8
👉Paper https://arxiv.org/pdf/2312.05251.pdf
👉Project https://geopavlakos.github.io/hamer
👉Demo huggingface.co/spaces/geopavlakos/HaMeR
👉Colab colab.research.google.com/drive/1rQbQzegFWGVOm1n1d-S6koOWDo7F2ucu
🔥 EfficientSAM: 20x faster Segment Anything 🔥
👉Meta AI Research unveils a novel family of SAM-like models, light-weight SAM models with SOTA quality-efficiency trade-offs. Up to 20x faster!
👉Review https://t.ly/966QS
👉Paper https://lnkd.in/duijp_Rh
👉Project https://lnkd.in/dW-p2CuH
👉Code https://lnkd.in/dAbZaB2t
👉Demo https://lnkd.in/d-tjKiUd
🩰 Magic Animating Human 🩰
👉MagicAnimate: the new SOTA in human animation. Code available: let's dance!
👉Review https://t.ly/Oq7Za
👉Paper https://lnkd.in/dSUbGgCs
👉Project https://lnkd.in/dkVFf-SV
👉Code https://lnkd.in/dj2dbzdg
👉Demo https://lnkd.in/dHEKPE9q
Hello everybody,
a lot of you asked me to re-open the sharing of the contents to involve more people. I want to follow your suggestion, hope you will enjoy this new mood!
👍 FREE TO FORWARD TO OTHER TELEGRAM CHANNELS
🔥 NO COPY OF THE POSTS
🔥 NO COMMERCIAL USAGE
🔥 NO UNRESPECTFUL USAGE
⚠️ UNDO THE FORWARDING OPTION AT THE FIRST VIOLATION ⚠️
🔎 Generative Powers of Ten 🔍
👉A text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene. From universe to a human cell 🤯
👉Review https://t.ly/2DG44
👉Paper https://lnkd.in/eDcSpU59
👉Project https://lnkd.in/e6NKu8n9
🍡 Animate Anyone: new SOTA! 🍡
👉Alibaba unveils Animate Anyone: novel #AI for transforming character images into animated videos controlled by desired pose sequences. Animating any character image into a video, unconstrained by specific domains 🚀
👉Review https://t.ly/qCahZ
👉Paper https://lnkd.in/d-zi8EZ6
👉Project https://lnkd.in/djwjQRvq
👉Code https://lnkd.in/dDMkjnKz
👑 HD Generative #AI With No $$$ 👑
👉DemoFusion: a novel approach for HD image generation w/ no money. Progressive Upscaling, Skip Residual, & Dilated Sampling to achieve higher-resolution ever 🔥
👉Review https://t.ly/sIqDV
👉Paper https://lnkd.in/deDt-zcK
👉Project https://lnkd.in/dFGj47Xw
👉Code https://lnkd.in/dY3UcXwp
🧱 Material Palette from Images 🧱
👉A novel problem in #AI: material extraction from a real-world image without any prior knowledge 🤯
👉Discussion https://t.ly/AIWs-
👉Paper https://lnkd.in/dBFAVWPF
👉Project https://lnkd.in/dV5jK8Sm
👉Code https://lnkd.in/dNhMnfFb
👉Dataset (coming) ...
🌳 NebulOS: (more than) Green AI 🌳
👉A novel hardware-aware Training-Free NAS approach that considers both training-free metrics & HW constraints, aiming to find the optimal balance between validation accuracy & energy consumption. 🚀
👉Review https://t.ly/Ozso1
👉Project sites.google.com/view/nebulos
👉Code https://github.com/fracapuano/NebulOS
👉Video https://lnkd.in/exN4Q2Fu
👉Hugging Face demo https://lnkd.in/eyCcPEPc
🎡 Panoptic Video Scene Graph 🎡
👉Combining video scene graph generation w/ panoptic segmentation for holistic video understanding. Novel HQ dataset with fine, temporal scene graph annotations & panoptic segmentation. Code released!🔥
👉Review https://t.ly/tckDT
👉Project jingkang50.github.io/PVSG/
👉Paper arxiv.org/pdf/2311.17058.pdf
👉Code github.com/LilyDaytoy/OpenPVSG
👉Tool github.com/lilyDaytoy/PVSGAnnotation
🔥 Stable (Stability.AI) Video Diffusion 🔥
👉 #StabilityAI released Stable Video Diffusion: latent video diffusion model for high-resolution, SOTA text-to-video and image-to-video generation
👉 Review https://t.ly/XwHys
👉 Code https://lnkd.in/dQw_yNuV
👉 Paper https://lnkd.in/dHn6f787
