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

Malasia2 234 Tecnologías y Aplicaciones7 718...

📈 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 17 168 suscriptores, ocupando la posición 7 718 en la categoría Tecnologías y Aplicaciones y el puesto 2 234 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 168 suscriptores.

Según los últimos datos del 20 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -169, y en las últimas 24 horas de 0, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 22.86%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 926 visualizaciones. En el primer día suele acumular 0 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 21 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.

17 168

Suscriptores

Sin datos24 horas

-357 días

-16930 días

3 926

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

22.86%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 166

🔥21,000+ Hours Dataset🔥 👉SpatialVID is a novel large-scale video dataset with explicit spatial annotations including camera poses, depth maps, structured captions and serialized motion instructions. The dataset consists of 7,089 hours of real-world dynamic scenes. Repo & Dataset Apache-2.0 💙 👉Review https://t.ly/Y9o5k 👉Paper arxiv.org/pdf/2509.09676 👉Project nju-3dv.github.io/projects/SpatialVID/ 👉Repo github.com/NJU-3DV/spatialVID

17 166

🐙Human-Centric Video Generation🐙 👉Tsinghua & #ByteDance unveil HuMo: a unified, human-centric video generation framework designed to produce HQ fine-grained, and controllable human videos from multimodal inputs. It supports strong text prompt following, consistent subject preservation, synchronized audio-driven motion. Repo released under Apache2.0💙 👉Review https://t.ly/3S8Yb 👉Paper https://arxiv.org/pdf/2509.08519 👉Project https://phantom-video.github.io/HuMo/ 👉Repo https://github.com/Phantom-video/HuMo

17 166

🌱 FoMo4Wheat Foundational Model 🌱 👉PheniX Lab et al. unveil a novel family of foundational models tailored for wheat image tasks, suitable for classification, detection, counting and segmentation. Demo, Dataset, Model & Code under MIT💙 👉Review https://t.ly/UzM-Z 👉Paper arxiv.org/pdf/2509.06907 👉Project fomo4wheat.phenix-lab.com/ 👉Repo github.com/PheniX-Lab/FoMo4Wheat? 👉Demo fomo4wheat.phenix-lab.com/demos

17 166

👻 From Skin to Skeleton 👻 👉This paper try unifying the SMPL body model with BSM, a new Biomechanical Skeleton Model. The SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. Model, code, and data available for research💙 👉Review https://t.ly/JsI8M 👉Paper arxiv.org/pdf/2509.06607 👉Project https://skel.is.tue.mpg.de/

17 166

🩸Foundation Red Blood Cells🩸 👉RedDino from University of Cagliari is a self-supervised foundation model designed for red blood cell (RBC) morphology analysis. Trained on 1.25M RBC images, it's the new SOTA in shape classification. Code & Models released under Apache2.0💙 👉Review https://t.ly/uWAch 👉Paper https://arxiv.org/pdf/2508.08180 👉Code https://github.com/Snarci/RedDino 👉Models huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc

17 166

🖌️Real-Time Drag-Based Editing🖌️ 👉The Visual AI Lab unveils Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping/inpainting. Inspired by elastic object deformation. Demo and Code released (unknown license)💙 👉Review https://t.ly/H5nlR 👉Paper https://arxiv.org/pdf/2509.04582 👉Project https://visual-ai.github.io/inpaint4drag/ 👉Repo https://github.com/Visual-AI/Inpaint4Drag 👉Demo https://colab.research.google.com/drive/1fzoyNzcJNZjM1_08FE9V2V20EQxGf4PH

17 166

Friends, I’ve just open my IG account: https://www.instagram.com/aleferra.ig | Feel free to add me What about posting stuff about AI on IG? Thoughts?

17 166

✂️ #AI Open-Source Annotation ✂️ 👉VisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for #computervision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0💙 👉Review https://t.ly/MoMvv 👉Paper https://lnkd.in/dxTncSgv 👉Repo https://lnkd.in/dCWMXp3x

17 166

✂️ #AI Open-Source Annotation ✂️ 👉VisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for hashtag#computervision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0💙 👉Review https://t.ly/MoMvv 👉Paper https://lnkd.in/dxTncSgv 👉Repo https://lnkd.in/dCWMXp3x

17 166

🔥WebEyeTrack: real-time/web eye🔥 👉WebEyeTrack is a novel framework that integrates lightweight SOTA gaze estimation models directly in the browser. Bringing deep‑learning gaze estimation to the web browser and explicitly accounts for head pose. Source Code released under MIT license💙 👉Review https://t.ly/Xon9h 👉Paper https://arxiv.org/pdf/2508.19544 👉Project redforestai.github.io/WebEyeTrack/ 👉Repo github.com/RedForestAi/WebEyeTrack

17 166

🍐 Promptable Human Mesh 🍐 👉PromptHMR is a promptable human pose/shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes “side information” readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. Code released under Non-Commercial Scientific Research Use Only 💙 👉Review https://t.ly/zJ7S- 👉Paper arxiv.org/pdf/2504.06397 👉Project yufu-wang.github.io/phmr-page/ 👉Repo github.com/yufu-wang/PromptHMR

17 166

🐉 #DoubleDragon with #AI 🐉 👉How Double Dragon would look like in real life? Each character has been transformed with #AI to capture their style, fighting spirit, and charisma, as if they had stepped right out of the game’s streets into the real world. AUDIO ON. Damn romantic💙 #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM 👉Post https://t.ly/0IpER 👉Channel http://www.youtube.com/@iaiaoh84

17 166

🧬 OpenVision 2 is out! 🧬 👉UCSC releases OpenVision2: a novel family of generative pretrained visual encoders that removes the text encoder and contrastive loss, training with caption-only supervision. Fully open, Apache 2.0💙 👉Review https://t.ly/Oma3w 👉Paper https://arxiv.org/pdf/2509.01644 👉Project https://ucsc-vlaa.github.io/OpenVision2/ 👉Repo https://github.com/UCSC-VLAA/OpenVision

17 166

Could you please help me with this poll? https://t.ly/3c3Aa Thanks, A.

17 166

🫛TMR: Few-Shot Template-matching🫛 👉POSTECH unveils TMR, a novel and simple template-matching detector for few-shot pattern detection, achieving strong (and SOTA) results on diverse datasets. A new dataset (RPINE) released, repo soon💙 👉Review https://t.ly/WWAcL 👉Paper https://lnkd.in/dJbSu5vk 👉Project https://lnkd.in/dwcDnHHQ 👉Repo https://lnkd.in/dp7aw8Cs

17 166

🪴 Pixie: Physics from Pixels 🪴 👉UPenn + MIT unveil Pixie: training a neural-net that maps pretrained visual features (i.e., CLIP) to dense material fields of physical properties in a single forward pass, enabling real‑time physics simulations. Repo & Dataset under MIT license💙 👉Review https://t.ly/1W0n5 👉Paper https://lnkd.in/dsHAHDqM 👉Project https://lnkd.in/dwrHRbRc 👉Repo https://lnkd.in/dy7bvjsK

17 166

❤️‍🔥PHD: Personalized 3D Humans❤️‍🔥 👉ETH & #Meta unveil PHD, a novel approach for personalized 3D human mesh recovery (HMR) and body fitting that leverages user-specific shape information to improve pose estimation accuracy. Code & models to be released💙 👉Review https://t.ly/IeRhH 👉Paper https://arxiv.org/pdf/2508.21257 👉Project https://phd-pose.github.io/ 👉Repo TBA

17 166

🌈 Multi-View 3D Tracking 🌈 👉MVTracker is the first data-driven multi-view 3D point tracker for tracking arbitrary 3D points across multiple cameras. Repo available💙 👉Review https://t.ly/rISMR 👉Paper arxiv.org/pdf/2508.21060 👉Project https://lnkd.in/drHtAmRC 👉Repo https://lnkd.in/d4k8mg3B

17 166

🉐Dress&Dance: Dress-up & Dance🉐 👉Dress&Dance: diffusion framework that generates HQ 5-second-long 24 FPS VTON videos at 1152×720 of a user wearing desired garments while moving in accordance with a given reference video. Impressive results but no repo announced🥺 👉Review https://t.ly/7NeTL 👉Paper https://arxiv.org/pdf/2508.21070 👉Project https://immortalco.github.io/DressAndDance/ 👉Repo 🥺

17 166

🌹ROSE: Remove Objects & Effects🌹 👉A novel framework that systematically fix the object’s effects on environment: shadows, reflections, light, translucency and mirror. Model, Demo & Dataset available via Hugging Face💙 👉Review https://t.ly/_KFM0 👉Paper https://lnkd.in/dNcTXQAE 👉Project https://lnkd.in/dFGmYT5h 👉Model https://lnkd.in/dhTT-VkN 👉Demo https://lnkd.in/dimgXZT6 👉Data https://lnkd.in/da7Jv667