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

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Малайзия2 238 Технологии и приложения7 725...

📈 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 17 173 подписчиков, занимая 7 725 место в категории Технологии и приложения и 2 238 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 173 подписчиков.

Согласно последним данным от 19 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -177, а за последние 24 часа — -9, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 21.83%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 3 749 просмотров. В течение первых суток публикация набирает 0 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 26.
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 20 июня, 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.

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Подписчики

-924 часа

-397 дней

-17730 день

3 749

Просмотры поста

Нет данных24 часа

Нет данных48 часов

21.83%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

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💜MoRo: Human Motion Recovery💜 👉Masked modeling for human motion Recovery under Occlusions. Given a monocular video captured from a static camera, MoRo (by ETHZ & #Meta) robustly reconstructs accurate/physically plausible human motion, even under challenging occlusions. Repo released💙 👉Review https://t.ly/kK_je 👉Paper arxiv.org/pdf/2601.16079 👉Project mikeqzy.github.io/MoRo/ 👉Repo github.com/mikeqzy/MoRo

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🦧VideoMaMa: Mask-Guided Matting🦧 👉VideoMaMa is novel a diffusion-based model that converts binary segmentation masks into continuous alpha mattes. Repo, Dataset & Demo💙 👉Review https://t.ly/l_0f8 👉Paper arxiv.org/pdf/2601.14255 👉Project cvlab-kaist.github.io/VideoMaMa 👉Repo github.com/cvlab-kaist/VideoMaMa 👉Demo huggingface.co/spaces/SammyLim/VideoMaMa

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💊Foundation Medical SAM3 💊 👉Medical SAM3: foundation model for universal prompt-driven medical image segmentation, by fully fine-tuning SAM3 on large-scale, heterogeneous 2D/3D medical imaging datasets with paired segmentation masks-text prompts. Repo & Demo announced💙 👉Review https://t.ly/C6jcy 👉Paper https://arxiv.org/pdf/2601.10880 👉Project chongcongjiang.github.io/MedicalSAM3/# 👉Repo github.com/AIM-Research-Lab/Medical-SAM3

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💚 #META 3D Casual Captures 💚 👉#META unveils ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Impressive results. Repo under CC BY-NC 4.0💙 👉Review https://t.ly/j08sJ 👉Paper arxiv.org/pdf/2601.11514 👉Project facebookresearch.github.io/ShapeR/ 👉Repo github.com/facebookresearch/ShapeR

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👹SOTA Part-level Generator👹 👉A novel a text-to-motion model that learns to compose complex motions through hierarchical conditioning on part-, action- & sequence-level text, enabling fine-grained control over body parts & timing. Code, models & Dataset to be released💙 👉Review https://t.ly/leB_R 👉Paper arxiv.org/pdf/2601.10909 👉Project coral79.github.io/frankenmotion/ 👉Repo github.com/Coral79/FrankenMotion-Code

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💢3D Human Gen-Seg💢 👉CoMoVi takes an input image with a text description and generates 3D human motion & video sequence synchronously within a single diffusion denoising loop. Repo & Dataset releasing💙 👉Review https://t.ly/khSkm 👉Paper arxiv.org/pdf/2601.10632 👉Project igl-hkust.github.io/CoMoVi/ 👉Repo github.com/IGL-HKUST/CoMoVi 👉Data huggingface.co/datasets/AfterJourney/CoMoVi-Dataset

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💜Interactive Humanoid Generation💜 👉FlowAct-R1 by ByteDance is a novel framework that enables lifelike, responsive, and high-fidelity humanoid video generation for seamless real-time interaction. No code but impressive results (see video with audio) 💙 👉Review https://t.ly/aQhol 👉Paper arxiv.org/pdf/2601.10103 👉Project grisoon.github.io/FlowAct-R1/

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🍿100M Video Action Dataset🍿 👉Action100M by META is a large-scale dataset w/ 1.2M instructional videos (14.6 years of duration), yielding O(100M) temporally localized segments with open-vocabulary action supervision and rich captions. Repo under FAIR NC Research License💙 👉Review https://t.ly/w5KXe 👉Paper https://arxiv.org/pdf/2601.10592 👉Repo https://github.com/facebookresearch/Action100M

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🎇 Multi-target SAM3 🎇 👉SAM3-DMS is a novel training-free decoupled strategy that utilizes fine-grained memory selection on individual objects. Robust identity preservation and tracking stability. Repo under SAM License💙 👉Review https://t.ly/jJOAr 👉Paper https://arxiv.org/pdf/2601.09699 👉Repo https://github.com/FudanCVL/SAM3-DMS

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💚 Segment Anything w/ Geometry💚 👉3AM (NYCU + #Nvidia) offers cross-view correspondence even under large viewpoint changes, cluttered scenes, and variations in capture conditions, enabling robust object tracking from both videos & casual multi-view images. Repo (coming) & Demo available💙 👉Review https://t.ly/olZwE 👉Paper https://arxiv.org/pdf/2601.08831 👉Project https://jayisaking.github.io/3AM-Page/ 👉Repo https://github.com/jayisaking 👉Demo https://huggingface.co/spaces/nycu-cplab/3AM

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👉Games Workshop (Warhammer) is banning the use of AI in creative and design processes to protect IP and human creativity. A decision that goes against the current hype of widespread AI adoption. And what about your organization? I need your help👇 Vote: https://www.linkedin.com/posts/visionarynet_ai-activity-7417106327019196417-TpGL

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In 2026, who should we keep an eye on? Vote: https://www.linkedin.com/posts/visionarynet_ai-deeplearning-aiwithpapers-activity-7416886610795077632-qQeP/

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🫛Active Object Reconstruction🫛 👉ObjSplat (Beijing) autonomously plans viewpoints and progressively reconstructs an unknown object into a Hi-Fi Gaussian model and water-tight mesh, enabling direct use in physics simulations. Repo announced💙 👉Review https://t.ly/au6HE 👉Paper arxiv.org/pdf/2601.06997 👉Project li-yuetao.github.io/ObjSplat-page/ 👉Repo https://github.com/Li-Yuetao/ObjSplat

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🔥Orient Anything V2 is out🔥 👉Orient Anything V2 is a foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Repo under CC-BY-4.0💙 👉Review https://t.ly/Ht7Xd 👉Paper arxiv.org/pdf/2601.05573 👉Project orient-anythingv2.github.io/ 👉Repo github.com/SpatialVision/Orient-Anything-V2

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🔥 New #AI Startups in 2026? 🔥 In 2026, which area would you focus on? 🤖Agents → workflows, copilots, etc. 🏭Vertical AI → Pharma, Automotive, Energy ... 🧠Infrastructure → MLOps, Security, Cost Control ... 🎨AI for Creators/Media → Video, avatars, contents ... Please, help me understanding what's next with this poll on LinkedIn :) https://www.linkedin.com/posts/visionarynet_ai-ai-deeplearning-activity-7415377341779996672-sQO1 LUV U \m/

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🌍Label Any Object in 3D 🌍 👉LabelAny3D: novel analysis-by-synthesis framework that reconstructs holistic 3D scenes from 2D to efficiently produce HQ 3D BBs annotations. Repo under CC-BY-4.0 license💙 👉Review https://t.ly/bO93j 👉Paper https://lnkd.in/dYb97zWG 👉Project https://lnkd.in/dJ9UKERb 👉Repo https://lnkd.in/d9SxtmiA

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🔥 Back from Holidays mood 🔥

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🦙 Depth as Neural Implicit 🦙 👉InfiniDepth represents depth as neural implicit fields, "infinite" (i.e.16K) resolution and geometrical details. Repo under Apache 2.0💙 👉Review https://t.ly/4we5t 👉Paper https://lnkd.in/dpiHQExj 👉Project https://lnkd.in/dy3JxKye 👉Repo https://lnkd.in/dAXbnK5z

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⭐ TOP 5 Papers you loved in 2025 ⭐ 👉 In 2025 novel architectures have redefined efficiency and accuracy, and almost every day brought a new SOTA in image understanding, tracking, and #GenAI. It’s been an inspiring ride, and 2026 it will be even wilder. This community (LinkedIn + Telegram) is now around 80,000+ people. 𝐏𝐚𝐩𝐞𝐫𝐬 (𝐛𝐲 𝐲𝐨𝐮𝐫 𝐩𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞): ⭐3D LLM Understanding https://t.ly/ejr1s ⭐DynOMo is out https://t.ly/t5pCf ⭐Tracking Transformations https://t.ly/NPyW4 ⭐YOLOv12 (new SOTA) https://t.ly/jj1oR ⭐Gaussian Surface Tracking https://t.ly/udpMq Thank you all💙

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