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 17 030 подписчиков, занимая 7 452 место в категории Технологии и приложения и 2 166 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 030 подписчиков.
Согласно последним данным от 02 сентября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -1, а за последние 24 часа — 5, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 12.68%. В первые 24 часа после публикации контент обычно набирает 7.60% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 159 просмотров. В течение первых суток публикация набирает 1 295 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 13.
- 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 03 сентября, 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.
17 030
Подписчики
+524 часа
+127 дней
-130 дней
Архив постов
💋SAM-MT: Real-Time Multi-Target VOS💋
👉Fudan & Shangai unveil SAM-MT, an efficient interactive multi-target video segmentation framework that maintains near-single-object efficiency (FPS/VRAM) as target count increases, while maintaining robust video segmentation performance. Repo available💙
👉Review https://t.ly/Z_4C7
👉Paper https://lnkd.in/dvS-iyBD
👉Project https://lnkd.in/daQ8na8T
👉Repo https://lnkd.in/dgbX2tZv
🔥ZipDepth: Depth on Any Device🔥
👉ZipDepth from UniBO is a compact monocular depth network that bridges this gap by combining an efficient reparameterizable encoder-decoder with large-scale knowledge distillation from a foundation model. Repo under MIT💙
👉Review https://t.ly/qYrLZ
👉Paper https://arxiv.org/pdf/2607.08771
👉Project https://zipdepth.github.io/
👉Repo https://github.com/fabiotosi92/ZipDepth
🏵️SoccerNet 2026 Results🏵️
👉The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video understanding💙
👉Review https://t.ly/sfD4T
👉Paper https://lnkd.in/dSBgW_3s
👉Project https://lnkd.in/dfdmuvG8
🐈⬛Spatial-perception native ViT🐈⬛
👉LingBot-Vision, a vision foundation model pretrained to be spatial-perception native. Better than 7x bigger foundational models. Repo under Apache💙
👉Review https://t.ly/9xIso
👉Paper https://arxiv.org/pdf/2607.05247
👉Project https://technology.robbyant.com/lingbot-vision
👉Repo https://github.com/robbyant/lingbot-vision
🏯Worldwide Semantic Facade🏯
👉A centimeter-accurate / cross-continental facade point clouds, with fine-grained semantic segmentation of architectural elements, and hierarchical facade taxonomy. 2.7B Dataset💙
👉Review https://t.ly/PpyFD
👉Paper https://arxiv.org/pdf/2607.02018
👉Project jiangyuanwangyi.github.io/UnderOneFacade_official
👉Data drive.google.com/drive/folders/1Yzz7PmyeK1qeOtkTFCfkbw7IEHXcMJo8
🔥Nvidia SpatialClaw is out🔥
👉From Nvidia a novel training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw lets a VLM-backed agent write Python in a persistent kernel, composing perception modules, inspecting intermediate results, and revising its strategy across steps. Impressive: +11.2 points on 20 benchmarks💙
👉Review https://t.ly/7JB0x
👉Paper https://arxiv.org/pdf/2606.13673
👉Project https://spatialclaw.github.io/
👉Repo https://github.com/NVlabs/SpatialClaw
🌒LUNA: Universal 3D Human Animation🌔
👉LUNA by HKUST + META is a novel LBS-free universal neural animation model that directly maps multiple 2D controls like images, keypoints, sketch and unseen characters into 3D-G deformations, bypassing explicit body fitting.
👉Review https://t.ly/ZX9Ex
👉Paper https://arxiv.org/pdf/2606.31981
👉Project https://penghtyx.github.io/LUNA/
👉Repo N/A 🥲
🛸PriorEye: Geospatial Self-Driving🛸
👉MRG (Oxford) introduces geospatial visual priors to leverage the street-level images in autonomous driving. Consistent improvement in performance. Repo under Apache💙
👉Review https://t.ly/7Jgav
👉Paper https://lnkd.in/dYeD2m7n
👉Project https://lnkd.in/dWJvNemr
👉Repo https://lnkd.in/dNExGGtx
🍀OctoSense: Open Sensing🍀
👉OctoSense is an open-source sensor platform with stereo RGB and event cameras, LiDAR, a thermal camera, an inertial measurement unit, RTK-corrected global positioning system, and proprioception.
👉Review https://t.ly/oFN8L
👉Paper https://lnkd.in/dM3zpyju
👉Project https://lnkd.in/ddrQ3uJ6
👉Repo https://lnkd.in/dhSDjSfG
👋 Hi everyone!
Over the past few weeks, the number of join requests has increased dramatically, which unfortunately also means a much higher number of spam and bots (in the last days around five hundreds been cut off)
To help me distinguish real people from fake profiles - and avoid rejecting genuine requests by mistake - I'd really appreciate if your profile includes:
📷 A real profile photo
👤 Your full name (or something reasonably identifiable)
💬 If you contact me, please use English if possible.
I don't speak Russian, Arabic, or Chinese, so if your profile and messages are only in those languages, it's very difficult for me to tell whether you're a real person or an automated account. Thank you for your understanding and for helping keep this damn community welcoming and spam-free!
With love,
Alessandro 😈
🔊VolHuMe - Volumetric Human Meshes🔊
👉VolHuMe (H/T @Martinella_94) is a novel, high-resolution large-scale dataset of volumetric human meshes with complete 4D GT: multi-view RGB-D, textured meshes, dense point clouds, normal maps, rigged assets, garment segmentation, and SMPL-X fittings in one dataset. Insane💙
👉Review https://t.ly/b5vxy
👉Paper https://arxiv.org/pdf/2606.23062
👉Project giuli13.github.io/volhume-website/#
👉Repo TBA soon
🕷️Human Universal Grasping🕷️
👉HUG is a flow-matching model that generates diverse human grasps for any user-specified object in a single RGB-D image captured from a stereo camera.
👉Review https://t.ly/VG1Eu
👉Paper https://arxiv.org/pdf/2606.17054
👉Repo https://github.com/KevinyWu/hug
👉Project https://grasping.io/
🔍 Nvidia Locate Anything 🔍
👉Diverse localization tasks under a unified vision-language model, including document understanding, GUI grounding, dense detection, and OCR. Repo released💙
👉Review https://t.ly/PvwFo
👉Paper https://lnkd.in/dWfNpzPZ
👉Project https://lnkd.in/dM89BX-8
👉Repo https://lnkd.in/dC4KCQSM
🪔Latent Decoding with Pixel Diffusion🪔
👉PiD by Nvidia is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass. Repo under Apache 2.0💙
👉Review https://t.ly/y19mA
👉Paper https://lnkd.in/duVC25C2
👉Project https://lnkd.in/dW6TkzCB
👉Repo https://lnkd.in/dnGdgKRr
🍒Count Anything, Any Granularity🍒
👉Open-world counting as multi-grained counting, where visual exemplars specify target appearance and fine-grained text specifies the intended semantic granularity across five explicit levels. Repo/Data under Apache💙
👉Review https://t.ly/nqz80
👉Paper https://lnkd.in/dp7khTRU
👉Project https://lnkd.in/d_jfX_Yn
👉Repo https://lnkd.in/dkTRGZkG
👉Data https://lnkd.in/dB83jRyT
🦄Unified Correspondence Transformer🦄
👉UniCorrn is the first correspondence model with shared weights that unifies 2D-2D, 2D-3D, and 3D-3D geometric matching with an end-to-end transformer architecture. Repo under CC BY-NC-SA 4.0💙
👉Review https://t.ly/2OBdq
👉Paper https://arxiv.org/pdf/2605.04044
👉Project https://neu-vi.github.io/UniCorrn/
👉Repo https://github.com/neu-vi/UniCorrn
About the frequency of posting in the channel:
🪝Syn4D: Multiview Synthetic 4D Dataset🪝
👉Syn4D is novel multi-view synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations💙
👉Review https://t.ly/SL1mk
👉Paper https://arxiv.org/pdf/2605.05207
👉Project https://jzr99.github.io/Syn4D/
👉Repo https://github.com/jzr99/Syn4D
👉Data huggingface.co/datasets/Syn4D/Syn4D_RGBD/tree/main
🧘♀️Holistic Shot Boundary Detection🧘♀️
👉OmniShotCut detects shot changes of the video in diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.), and recognize Sudden Jump and Transitions (dissolve, fade, wipe, etc.) by proposing a Shot-Query-based Video Transformer. Repo, demo & benchmark💙
👉Review https://t.ly/sTi7N
👉Paper https://arxiv.org/pdf/2604.24762
👉Project uva-computer-vision-lab.github.io/OmniShotCut_website/
👉Repo github.com/UVA-Computer-Vision-Lab/OmniShotCut
🛒 Reshoot-Anything is out 🛒
👉Reshoot-Anything reshoots dynamic monocular videos under novel camera trajectories. Code under Apache 2.0 💙
👉Review https://t.ly/MIqAc
👉Paper https://arxiv.org/pdf/2604.21776
👉Project adithyaiyer1999.github.io/reshoot-anything/
👉Repo github.com/morphicfilms/video-to-video
