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 17 166 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 17 166 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.

17 166

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

17 157

🔥 GAGA: Group Any Gaussians 🔥 👉GAGA is a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models. Code available, recently updated💙 👉Review https://t.ly/Nk_jT 👉Paper www.gaga.gallery/static/pdf/Gaga.pdf 👉Project www.gaga.gallery/ 👉Repo github.com/weijielyu/Gaga

17 157

🧞‍♂️Omni-RGPT: SOTA MLLM Understanding🧞‍♂️ 👉 #NVIDIA presents Omni-RGPT, MLLM for region-level comprehension for both images & videos. New SOTA on image/video-based commonsense reasoning. 👉Review https://t.ly/KHnQ7 👉Paper arxiv.org/pdf/2501.08326 👉Project miranheo.github.io/omni-rgpt/ 👉Repo TBA soon

17 157

🆘 Help: Looking for Outstanding Speakers 🆘 👉Who would you suggest as a speaker for your ideal conference on AI (CV, LLM, RAG, ML, HW Optimization, AI & Space, etc.)? Only “hardcore” technical talks, no commercial at all. Please comment here with name, topic and affiliation (es: Paul Gascoigne, Computer Vision & Football, Scotland Team). ⭐Guaranteed tickets & more for the suggestions that will become invited speakers ;)

17 157

🏆Universal Detector-Free Match🏆 👉MatchAnything: novel detector-free universal matcher across unseen real-world single/cross-modality domains. Same weights for everything. Code announced, to be released 💙 👉Review https://t.ly/sx92L 👉Paper https://lnkd.in/dWwRwGyY 👉Project https://lnkd.in/dCwb2Yte 👉Repo https://lnkd.in/dnUXYzQ5

17 157

❤️‍🔥 Uncommon object in #3D ❤️‍🔥 👉#META releases uCO3D, a new object-centric dataset for 3D AI. The largest publicly-available collection of HD videos of objects with 3D annotations that ensures full-360◦ coverage. Code & data under CCA 4.0💙 👉Review https://t.ly/Z_tvA 👉Paper https://arxiv.org/pdf/2501.07574 👉Project https://uco3d.github.io/ 👉Repo github.com/facebookresearch/uco3d

17 157

🔥 Depth Any Camera (SOTA) 🔥 👉DAC is a novel and powerful zero-shot metric depth estimation framework that extends a perspective-trained model to effectively handle cams with varying FoVs (including large fisheye & 360◦). Code announced (not available yet)💙 👉Review https://t.ly/1qz4F 👉Paper arxiv.org/pdf/2501.02464 👉Project yuliangguo.github.io/depth-any-camera/ 👉Repo github.com/yuliangguo/depth_any_camera

17 157

⚽ FIFA 3D Human Pose ⚽ 👉#FIFA WorldPose is a novel dataset for multi-person global pose estimation in the wild, featuring footage from the 2022 World Cup. 2.5M+ annotation, released 💙 👉Review https://t.ly/kvGVQ 👉Paper arxiv.org/pdf/2501.02771 👉Project https://lnkd.in/d5hFWpY2 👉Dataset https://lnkd.in/dAphJ9WA

17 157

🔥 "Nuclear" AI vs. Hyper-Cheap Inference 🔥 ⭐ What do you expect in 2025 after the #Nvidia announcements at CES 2025? Free to comment :)

Anonymous voting

17 157

🧤World-Space Ego 3D Hands🧤 👉The Imperial College unveils HaWoR, a novel world-space 3D hand motion estimation for egocentric videos. The new SOTA on both cam pose estimation & hand motion reconstruction. Code under Attribution-NC-ND 4.0 Int.💙 👉Review https://t.ly/ozJn7 👉Paper arxiv.org/pdf/2501.02973 👉Project hawor-project.github.io/ 👉Code github.com/ThunderVVV/HaWoR

17 157

🥮 SOTA probabilistic tracking🥮 👉ProTracker is a novel framework for robust and accurate long-term dense tracking of arbitrary points in videos. Code released under CC Attribution-NonCommercial💙 👉Review https://t.ly/YY_PH 👉Paper https://arxiv.org/pdf/2501.03220 👉Project michaelszj.github.io/protracker/ 👉Code github.com/Michaelszj/pro-tracker

17 157

What is your favorite source for the AI updates?

Anonymous voting

17 157

⭐ Poll Alert!! ⭐ [EDIT] see below

17 157

⭐ Quick poll to start 2025 ⭐ What is your favorite source for the AI updates? Please vote here: https://t.ly/chQWq Thanks!

17 157

🌳 HD Video Object Insertion 🌳 👉VideoAnydoor is a novel zero-shot video object insertion #AI with high-fidelity detail preservation and precise motion control. All-in-one: video VTON, face swapping, logo insertion, multi-region editing, etc. 👉Review https://t.ly/hyvRq 👉Paper arxiv.org/pdf/2501.01427 👉Project videoanydoor.github.io/ 👉Repo TBA

17 157

⭐TOP 10 Papers you loved - 2024⭐ 👉Here the list of my posts you liked the most in 2024, thank you all 💙 𝐏𝐚𝐩𝐞𝐫𝐬: ⭐"Look Ma, no markers" ⭐T-Rex 2 Detector ⭐Models at Any Resolution 👉The full list with links: https://t.ly/GvQVy

17 157

🔄️ Orient Anything in 3D 🔄️ ️ 👉Orient Anything is a novel robust image-based object orientation estimation model. By training on 2M rendered labeled images, it achieves strong zero-shot generalization in the wild. Code released💙 👉Review https://t.ly/ro5ep 👉Paper arxiv.org/pdf/2412.18605 👉Project orient-anything.github.io/ 👉Code https://lnkd.in/d_3k6Nxz

17 157

🍄 Open-MLLMs Self-Driving 🍄 👉OpenEMMA: a novel open-source e2e framework based on MLLMs (via Chain-of-Thought reasoning). Effectiveness, generalizability, and robustness across a variety of challenging driving scenarios. Code released under Apache 2.0💙 👉Review https://t.ly/waLZI 👉Paper https://arxiv.org/pdf/2412.15208 👉Code https://github.com/taco-group/OpenEMMA

17 157

🫶 Dynamic Cam-4D Hands 🫶 👉The Imperial College unveils Dyn-HaMR, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Code announced under MIT💙 👉Review https://t.ly/h5vV7 👉Paper arxiv.org/pdf/2412.12861 👉Project dyn-hamr.github.io/ 👉Repo github.com/ZhengdiYu/Dyn-HaMR

17 157

🐕 Gaze-LLE: Neural Gaze 🐕 👉Gaze-LLE: novel transformer framework that streamlines gaze target by leveraging features from frozen DINOv2 encoder. Code & models under MIT 💙 👉Review https://t.ly/SadoF 👉Paper arxiv.org/pdf/2412.09586 👉Repo github.com/fkryan/gazelle

17 157

🌹 4D Neural Templates 🌹 👉#Stanford unveils Neural Templates, generating HQ temporal object intrinsics for several natural phenomena and enable the sampling and controllable rendering of these dynamic objects from any viewpoint, at any time of their lifespan. A novel task in vision is born💙 👉Review https://t.ly/ka_Qf 👉Paper https://arxiv.org/pdf/2412.05278 👉Project https://chen-geng.com/rose4d#toi