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
Больше📈 Аналитический обзор Telegram-канала AI with Papers - Artificial Intelligence & Deep Learning
Канал AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 17 261 подписчиков, занимая 7 491 место в категории Технологии и приложения и 2 128 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 261 подписчиков.
Согласно последним данным от 24 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 258, а за последние 24 часа — -2, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 16.76%. В первые 24 часа после публикации контент обычно набирает 7.11% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 893 просмотров. В течение первых суток публикация набирает 1 227 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 16.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как framework, object, dataset, tba, depth.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 25 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
17 261
Подписчики
-224 часа
+27 дней
+25830 дней
Архив постов
🎤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
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⭐ Quick poll to start 2025 ⭐
What is your favorite source for the AI updates? Please vote here: https://t.ly/chQWq
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