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 208 Технологии и приложения7 678...

📈 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 17 119 подписчиков, занимая 7 678 место в категории Технологии и приложения и 2 208 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 119 подписчиков.

Согласно последним данным от 29 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -162, а за последние 24 часа — -10, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 17.42%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 2 983 просмотров. В течение первых суток публикация набирает 0 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 30 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.

17 119

Подписчики

-1024 часа

-277 дней

-16230 день

2 983

Просмотры поста

Нет данных24 часа

Нет данных48 часов

17.42%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

17 119

🤖 META Human-Robot 🤖 👉#META PARTNR: novel benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. The largest benchmark of its kind: 100,000+ natural language tasks, spanning 60 houses and 5,819 unique objects. Code & Data (🤗) under MIT💙 👉Review https://t.ly/zcN0K 👉Paper arxiv.org/pdf/2411.00081 👉Repo github.com/facebookresearch/partnr-planner 🤗Data huggingface.co/datasets/ai-habitat/partnr_episodes

17 119

👗3D Dynamic Garments👗 👉UCLA introduces Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image. 👉Review https://t.ly/qciHV 👉Paper arxiv.org/pdf/2502.03449 👉Project dress-1-to-3.github.io

17 119

🔥 VideoJAM: #META's Video-Model (SOTA) 🔥 👉#META's VideoJAM: the new SOTA (by large margin) in motion coherence for video generation, much better than SORA! A strong motion prior into any video-gen model. Impressive results, no code announced🥲 👉Review https://shorturl.at/id7Bt 👉Paper https://arxiv.org/pdf/2502.02492 👉Project https://hila-chefer.github.io/videojam-paper.github.io/

17 119

🛸Real-Time Differentiable Tracing🛸 👉 Radiant Foam is a novel scene representation by leveraging the decades-old efficient volumetric mesh ray tracing algorithm (largely overlooked in recent research). Performing like Gaussian Splatting, without the constraints of rasterization. Code announced💙 👉Review https://shorturl.at/26U06 👉Paper https://arxiv.org/pdf/2502.01157 👉Project https://radfoam.github.io/ 👉Repo https://github.com/theialab/radfoam

17 119

🐙MambaGlue: SOTA feats. matching🐙 👉MambaGlue is a hybrid neural network combining the Mamba and the Transformer architectures to match local features. Source Code announced, to be released💙 👉Review https://shorturl.at/LxDG1 👉Paper arxiv.org/pdf/2502.00462 👉Repo https://lnkd.in/dAujfGZQ

17 119

🈯 SOTA 0-Shot Multi-View 🈯 👉MVGD by #TOYOTA is the SOTA method that generates images and scale-consistent depth maps from novel viewpoints given an arbitrary number of posed input views. A novel diffusion-based architecture capable of direct pixel-level generation. Code announced 💙 👉Review https://t.ly/_ecKl 👉Paper arxiv.org/pdf/2501.18804 👉Project mvgd.github.io/ 👉Repo TBA

17 119

💎AI-driven Docs Conversion💎 👉Docling by IBM, is the ALL-in-ONE, open source solution for documents; parsing several types of popular formats into a unified, richly structured representation. Powered by SOTA models for layout (DocLayNet) and table structure (TableFormer), it runs efficiently on low-cost hardware. Code under MIT💙 👉Review https://t.ly/nSCfT 👉Paper https://lnkd.in/dc5Kpc2F 👉Repo https://lnkd.in/d9gvw9bt

17 119

Social feed of everyone is broken because of unnecessary/not required opinions about DeepSeek. Your wish:

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17 119

🌅 Generative Human Mesh Recovery 🌅 👉GenHMR is a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in 2D-to-3D mapping process. Impressive results but no code announced 🥺 👉Review https://t.ly/Rrzpj 👉Paper https://arxiv.org/pdf/2412.14444 👉Project m-usamasaleem.github.io/publication/GenHMR/GenHMR.html

17 119

☀️ Relightable Full-Body Avatars ☀️ 👉#Meta unveils the first approach ever to jointly model the relightable appearance of the body, face, and hands of drivable avatars. 👉Review https://t.ly/kx9gf 👉Paper arxiv.org/pdf/2501.14726 👉Project neuralbodies.github.io/RFGCA

17 119

🦕[SOTA] Visual Grounding VOS🦕 👉ReferDINO is the first end-to-end approach for adapting foundational visual grounding models to RVOS. Code & models to be released soon💙 👉Review https://t.ly/SDFy9 👉Paper arxiv.org/pdf/2501.14607 👉Project isee-laboratory.github.io/ReferDINO/ 👉Repo github.com/iSEE-Laboratory/ReferDINO

17 119

🎨MatAnyone: Human Matting🎨 👉MatAnyone is a novel approach for human video matting that supports the target assignment. Stable tracking in long videos even with complex/ambiguous BGs. Code & 🤗-Demo announced💙 👉Review https://t.ly/NVXsT 👉Paper arxiv.org/pdf/2501.14677 👉Project pq-yang.github.io/projects/MatAnyone 👉Repo TBA

17 119

🪆SOTA Points Segmentation🪆 👉VGG Oxford unveils a novel loss to segment objects in videos based on their motion and NO other forms of supervision! Training the net using long-term point trajectories as a supervisory signal to complement optical flow. New SOTA! 👉Review https://t.ly/8Bsbt 👉Paper https://arxiv.org/pdf/2501.12392 👉Code https://github.com/karazijal/lrtl 👉Project www.robots.ox.ac.uk/~vgg/research/lrtl/

17 119

🔥 The code of DynOMo is out 🔥 👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input 👉Review https://t.ly/t5pCf 👉Paper https://lnkd.in/dwhzz4_t 👉Repo github.com/dvl-tum/DynOMo 👉Project https://lnkd.in/dMyku2HW

17 119

🔥 The code of DynOMo is out 🔥 👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input. Source code released under BSD 3-Clause💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅TUM, CMU (H/T Jenny Seidenschwarz) & NVIDIA ✅Online 2D/3D point tracking from unposed monocular ✅Tracking-by-reconstruction baseline for online TAP ✅New baseline for online PT with unposed mono-cam hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse hashtag#LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://lnkd.in/dwhzz4_t 👉Repo github.com/dvl-tum/DynOMo 👉Project https://lnkd.in/dMyku2HW

17 119

🦠A-Life with Foundation Models🦠 👉A super team unveils ASAL, a new paradigm for Artificial Life research. A diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia & Neural Cellular Automata. Code under Apache 2.0💙 👉Review https://t.ly/7SZ8A 👉Paper arxiv.org/pdf/2412.17799 👉Project http://pub.sakana.ai/asal/ 👉Repo https://lnkd.in/dP5yxKtw

17 119

🎤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 🥺

17 119

🧵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

17 119

🔥 [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

17 119

🌈 #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