AI with Papers - Artificial Intelligence & Deep Learning
Ir al canal en Telegram
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 7 491 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 17 261 suscriptores.
Según los últimos datos del 24 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 258, 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 16.76%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.11% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 893 visualizaciones. En el primer día suele acumular 1 227 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 25 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
+27 días
+25830 días
Archivo de publicaciones
🎤EMO2: Audio-Driven Avatar🎤
👉Alibaba previews a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Turn your audio ON. Stunning results but no code 🥺
👉Review https://t.ly/x8slQ
👉Paper arxiv.org/pdf/2501.10687
👉Project humanaigc.github.io/emote-portrait-alive-2/
👉Repo 🥺
🧵Time-Aware Pts-Tracking🧵
👉Chrono: feature backbone specifically designed for point tracking with built-in temporal awareness. Long-term temporal context, enabling precise prediction even without the refinements. Code announced💙
👉Review https://t.ly/XAL7G
👉Paper arxiv.orgzpdf/2501.12218
👉Project cvlab-kaist.github.io/Chrono/
👉Repo github.com/cvlab-kaist/Chrono
🔥 [SOTA] Long-Video Depth Anything 🔥
👉ByteDance unveils Video Depth Anything: HQ, consistent depth estimation in SUPER-long videos (over several minutes) without sacrificing efficiency. Based on Depth Anything V2 with a novel efficient spatial-temporal head. Repo available under Apache 2.0💙
👉Review https://t.ly/Q4ZZd
👉Paper arxiv.org/pdf/2501.12375
👉Project https://lnkd.in/dKNwJzbM
👉Repo https://lnkd.in/ddfwwpCj
🌈 #Nvidia Foundation ZS-Stereo 🌈
👉Nvidia unveils FoundationStereo, a foundation model for stereo depth estimation with strong zero-shot generalization. In addition, a large-scale (1M stereo pairs) synthetic training dataset featuring large diversity and high photorealism. Code, model & dataset to be released💙
👉Review https://t.ly/rfBr5
👉Paper arxiv.org/pdf/2501.09898
👉Project nvlabs.github.io/FoundationStereo/
👉Repo github.com/NVlabs/FoundationStereo/tree/master
🧽 Diffusion Video Inpainting 🧽
👉#Alibaba unveils a technical report about DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. Code & weights released under Apache💙
👉Review https://t.ly/7rEll
👉Paper arxiv.org/pdf/2501.10018
👉Project lixiaowen-xw.github.io/DiffuEraser-page/
👉Repo github.com/lixiaowen-xw/DiffuEraser
🏄♀️ GSTAR: Gaussian Surface Tracking 🏄♀️
👉ETH Zurich unveils GSTAR, a novel framework for photo-realistic rendering, surface reconstruction, and 3D tracking for dynamic scenes while handling topology changes. Code announced💙
👉Review https://t.ly/udpMq
👉Paper arxiv.org/pdf/2501.10283
👉Project chengwei-zheng.github.io/GSTAR/
👉Repo TBA
🎁Free Book: LLM Foundations🎁
👉A fully free book just released on arXiv to outline the basic concepts of #LLMs and related techniques with a focus on the foundational aspects.
✅Chapter 1: basics of pre-training
✅Chapter 2: gen-models & LLMs
✅Chapter 3: prompting methods
✅Chapter 4: alignment methods
👉If you have any background in ML, along with a certain understanding of stuff like Transformers, this book will be "smooth". However, even without this prior knowledge, it is still perfectly fine because the contents of each chapter are self-contained.
👉Review https://t.ly/9LGCa
👉Book https://lnkd.in/d3VkswZf
🔥 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
🧞♂️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
🆘 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 ;)
🏆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
❤️🔥 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
🔥 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
⚽ 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
🔥 "Nuclear" AI vs. Hyper-Cheap Inference 🔥
⭐ What do you expect in 2025 after the #Nvidia announcements at CES 2025? Free to comment :)
🧤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
🥮 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
What is your favorite source for the AI updates?
⭐ Quick poll to start 2025 ⭐
What is your favorite source for the AI updates? Please vote here: https://t.ly/chQWq
Thanks!
