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 17 262 suscriptores, ocupando la posición 7 491 en la categoría Tecnologías y Aplicaciones y el puesto 2 128 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 262 suscriptores.
Según los últimos datos del 23 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 255, 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 18.93%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.17% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 267 visualizaciones. En el primer día suele acumular 1 237 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 20.
- 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 24 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.
17 262
Suscriptores
+224 horas
+67 días
+25530 días
Archivo de publicaciones
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🫛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
🪴 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
❤️🔥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
🌈 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
🉐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 🥺
🌹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
⚽SoccerNet 2025 results are out!⚽
👉SoccerNet 2025 Challenges is the open benchmarking dedicated to advancing computer vision research in football video understanding. Repo for training & Dataset💙
👉Review https://t.ly/MfHKg
👉Paper https://arxiv.org/pdf/2508.19182
👉Project https://www.soccer-net.org/
👉Repo https://github.com/SoccerNet
🥶 OmniHuman-1.5 🥶
👉#ByteDance proposes a novel framework designed to generate character animations that are not only physically plausible but also semantically coherent and expressive. Coherency with speech's rhythm, prosody and semantic content. Impressive results but no code 🥺
👉Review https://t.ly/CnRmX
👉Paper arxiv.org/pdf/2508.19209
👉Project omnihuman-lab.github.io/v1_5/
👉Repo 🥺
🏎️ VROOM: F1 Reconstruction🏎️
👉Berkeley unveils VROOM, the first attempt for reconstructing 3D models of #Formula1 circuits using only onboard camera footage from racecars. Extreme challenges due to noise & speed. Repo released💙
👉Review https://t.ly/uuHdT
👉Paper arxiv.org/pdf/2508.17172
👉Repo github.com/yajatyadav/vroom
👉Project varun-bharadwaj.github.io/vroom/
🧤Diffusive Hand from Signs🧤
👉LIGM + #NVIDIA unveil a novel generative model of 3D hand motions from Sign Language Data. Motion characteristics such as handshapes, locations, finger, hand & arm movements. Code, Models & Data to be released 💙
👉Review https://t.ly/HonX_
👉Paper https://arxiv.org/pdf/2508.15902
👉Project https://imagine.enpc.fr/~leore.bensabath/HandMDM/
👉Data drive.google.com/drive/u/1/folders/1BLsu2hAqhAJ_gnGb9TNXW7MLiSuSEzEj
👉Repo TBA
🫔ATLAS: SOTA Human Model🫔
👉#META presents ATLAS, a novel high-fidelity body model learned from 600k high-res. scans captured using 240 synchronized cams. Code announced, to be released💙
👉Review https://t.ly/0hHud
👉Paper https://arxiv.org/pdf/2508.15767
👉Project https://jindapark.github.io/projects/atlas/
👉Repo TBA
🔬Intern-S1: SOTA MM-MoE 🔬
👉InternS1: a MM-MoE with 28B activated / 241b total parameters, continually pre-trained on 5T tokens, including 2.5T+ tokens from scientific domains. New SOTA for professional tasks, such as molecular synthesis planning, reaction condition prediction, etc. Models available under Apache 2.0💙
👉Review https://t.ly/3l5UW
👉Paper arxiv.org/pdf/2508.15763
👉Repo github.com/InternLM/Intern-S1
🤗HF huggingface.co/internlm/Intern-S1
🧉 YOPO: SOTA 9-DoF Pose🧉
👉Pit In Co. unveils YOPO, a novel single-stage, query-based framework that treats category-level 9-DoF estimation as a natural extension of 2D detection. A practical solution for mono-RGB, category-level, multi-obj pose estimation. Code & models announced (coming)💙
👉Review https://t.ly/cf_Cl
👉Paper https://arxiv.org/pdf/2508.14965
👉Project mikigom.github.io/YOPO-project-page/
👉Repo TBA
📡 ROVR Open Dataset is out 📡
👉A novel large-scale open 3D dataset for autonomous driving, robotics, and 4D perception tasks. To be released for academic (for free) & commercial💙
👉Review https://t.ly/iDcvg
👉Paper https://arxiv.org/pdf/2508.13977
👉Project https://xiandaguo.net/ROVR-Open-Dataset
👠 OmniTry: Virtual Try-On Anything 👠
👉OmniTry: unified framework that extends VTON beyond garment to encompass any wearable objects (jewelries, accessories, etc.) in mask-free setting. Weights, HF demo & benchmark released💙
👉Review https://t.ly/wMBGQ
👉Paper https://lnkd.in/dQe9MchS
👉Project https://omnitry.github.io/
👉Repo https://lnkd.in/d3QwAXY2
🤗Demo https://lnkd.in/duUcZpVA
🌈DAViD: Synthetic Depth-Normal-Segmentation🌈
👉#Microsoft unveils DAViD: 100% synthetic dataset/models for human Depth, Normals & Segmentation. Impressive results at a fraction of the cost of the foundation models! Compliancy with privacy, copyright, licensing, and diversity requirements. Dataset available, models & runtime under MIT💙
👉Review https://t.ly/-SlO_
👉Paper https://lnkd.in/eCmMXpTg
👉Project https://lnkd.in/eurCSWkm
👉Repo https://lnkd.in/e7PWFgP2
🔀4DNeX: Feed-Forward 4D video🔀
👉4DNeX is the first feed-forward framework for generating 4D scene representations from a single image by fine-tuning diffusion model. HQ dynamic pt-clouds & downstream tasks such as novel-view video synthesis with strong generalizability. Code/Data announced 💙
👉Review https://t.ly/SpkD-
👉Paper arxiv.org/pdf/2508.13154
👉Project https://4dnex.github.io/
👉Repo github.com/3DTopia/4DNeX
👉Data https://lnkd.in/dh4_3Ghf
👉Demo https://lnkd.in/dztyzwgg
🏓TOTNet: Occlusion-aware Tracking🏓
👉TOTNet is a novel Temporal Occlusion Tracking Network that leverages 3D-convs, visibility-weighted loss, & occlusion augmentation to improve performance under occlusions. Code & Data available under MIT💙
👉Review https://t.ly/Q0jAf
👉Paper https://lnkd.in/dUYsa-GC
👉Repo https://lnkd.in/d3QGUHYb
🤖 Impact of SuperHuman AI 🤖
👉The NoProfit AI Futures Project unveils a (dystopic) scenario about what super-AI might look like. Forecast from today to the bio-engineered human-like creatures. A fascinating speculation of the future with the "slow-down" and "race" scenarios. Enjoy 💙
👉Review https://t.ly/EgmfJ
👉Project https://ai-2027.com/
