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 204 Технологии и приложения7 654...

📈 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 17 102 подписчиков, занимая 7 654 место в категории Технологии и приложения и 2 204 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 102 подписчиков.

Согласно последним данным от 04 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -134, а за последние 24 часа — -8, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 21.82%. В первые 24 часа после публикации контент обычно набирает 7.71% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 3 732 просмотров. В течение первых суток публикация набирает 1 319 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 05 июля, 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.

17 102

Подписчики

-824 часа

-287 дней

-13430 день

3 732

Просмотры поста

~ 1 31924 часа

~ 1 60248 часов

21.82%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

17 102

🔥 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/

17 102

🌍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

17 102

🔥 Back from Holidays mood 🔥

17 102

🦙 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

17 102

⭐ 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💙

17 102

🎯Generative Refocusing is out🎯 👉Generative Refocusing is a two-step process that uses DeblurNet to recover all-in-focus images from various inputs and BokehNet for creating controllable bokeh (in semi-supervised mode). Repo under Apache2.0💙 👉Review https://t.ly/8t7PA 👉Paper arxiv.org/pdf/2512.16923 👉Project generative-refocusing.github.io/ 👉Repo github.com/rayray9999/Genfocus 👉Demo huggingface.co/spaces/nycu-cplab/Genfocus-Demo

17 102

🏜️ Depth Any Panoramas 🏜️ 👉DAP is the new SOTA foundation model for panoramic depth estimation with a large scale dataset. Data & Repo under MIT💙 👉Review https://t.ly/LaUmd 👉Paper arxiv.org/pdf/2512.16913 👉Project https://lnkd.in/dvqNV9jx 👉Repo https://lnkd.in/dmNzhb-7 👉Demo https://lnkd.in/dDwjMF3u

17 102

🫠 FlexAvatar: 3D Heads Avatars 🫠 👉TUM introduces FlexAvatar, a novel method for creating HQ and complete 3D head avatars from a single image. Code announced💙 👉Review https://t.ly/Rkdtd 👉Paper arxiv.org/pdf/2512.15599 👉Project tobias-kirschstein.github.io/flexavatar/ 👉Repo TBA

17 102

👀DriverGaze360: Driver SOTA👀 👉DriverGaze360 is a large-scale 360◦ field of view driver attention dataset, containing ∼1M gaze-labeled frames. Code & Dataset announced💙 👉Review https://t.ly/ZcoUw 👉Paper arxiv.org/pdf/2512.14266 👉Project av.dfki.de/drivergaze360/ 👉Repo github.com/dfki-av/drivergaze360 👉Data av.dfki.de/drivergaze360/dataset

17 102

💷 SOTA Zero-Shot Stereo Matching💷 👉Fast-FoundationStereo by #Nvidia is a novel family of architectures that achieve, for the first time, strong zero-shot generalization at real-time frame rate via divide-&-conquer acceleration. Code & Data announced💙 👉Review https://t.ly/XD6pO 👉Paper https://lnkd.in/d9_YKW2A 👉Project https://lnkd.in/dKDxm7EX 👉Repo https://lnkd.in/dR4-PdsW

17 102

💚 MatAnyone 2 is out! 💚 👉MatAnyone 2 is the most advanced human video matting framework that preserves fine details by avoiding segmentation-like boundaries, while also shows enhanced robustness under challenging real-world conditions. Repo & Dataset announced💙 👉Review https://www.linkedin.com/posts/visionarynet_artificialintelligence-ai-deeplearning-activity-7406244283055525888-8Dl_ 👉Paper arxiv.org/pdf/2512.11782 👉Project pq-yang.github.io/projects/MatAnyone2 👉Repo github.com/pq-yang/MatAnyone2

17 102

🫎 MoCapAnything is out 🫎 👉MoCapAnything is novel a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware IK fitting. No code announced 🥲 👉Review https://t.ly/_Tw6t 👉Paper arxiv.org/pdf/2512.10881 👉Project animotionlab.github.io/MoCapAnything

17 102

🧱 Pixel Art Volumetric Rendering 🧱 👉Voxify3D is a novel differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Repo announced💙 👉Review https://t.ly/qPyNl 👉Paper https://lnkd.in/du5ikJGN 👉Project https://lnkd.in/dpiAjj5m 👉Repo TBA

17 102

🎷Layered PSD Diffusion🎷 👉OmniPSD produces layered PSD files with transparent alpha channels, separating text, foreground elements, and background into clean RGBA layers that can be directly edited in tools. Online Demo💙 👉Review https://t.ly/YNRAC 👉Paper arxiv.org/pdf/2512.09247 👉Project showlab.github.io/OmniPSD/ 👉Demo https://www.lovart.ai/it

17 102

🐘TTSC for 3D Generative🐘 👉SpaceControl is the new SOTA training-free test-time method for explicit spatial control of 3D generation. Repo announced💙 👉Review https://t.ly/1zrah 👉Paper https://lnkd.in/dEWh3vep 👉Project https://lnkd.in/dScftUmm 👉Repo TBA

17 102

✌️SOTA Generative SLP✌️ 👉Stable Signer is a new sign language generative model. It redefines the SLP task as a hierarchical generation end-to-end task that only includes text understanding (Prompt2Gloss, Text2Gloss) and Pose2Vid. Repo with data 💙 👉Review https://t.ly/yKZhn 👉Paper arxiv.org/pdf/2512.04048 👉Project stablesigner.github.io/ 👉Data github.com/SignLLM/Prompt2Sign/tree/main/tools-new-2025

17 102

🦄 Native Unified Multimodal 🦄 👉META unveils a novel UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows E2E processing of images/videos for both understanding/generation. Code under legal review💙 👉Review https://t.ly/7wmKP 👉Paper https://lnkd.in/djT4WGEU 👉Project https://tuna-ai.org/ 👉Repo github.com/wren93/tuna

17 102

🥭3D Point Motion Editing🥭 👉Edit-by-Track enables precise video motion editing via 3D point tracks. By specifying desired 3D trajectories, users can seamlessly control joint camera and object motion, remove objects, and transfer motion between videos. No code announced but it's a relevant paper💙 👉Review https://t.ly/lqzHY 👉Paper https://arxiv.org/pdf/2512.02015 👉Project https://edit-by-track.github.io/

17 102

🌵Instance-Level Video Generation🌵 👉InstanceV is the first video generation framework to be designed specifically for instance-level control at the architectural level. Code & Data announced💙 👉Review https://t.ly/y_TBT 👉Paper arxiv.org/pdf/2511.23146 👉Project aliothchen.github.io/projects/InstanceV/ 👉Repo TBA