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 239 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 17 181 подписчиков, занимая 7 725 место в категории Технологии и приложения и 2 239 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 181 подписчиков.

Согласно последним данным от 16 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -156, а за последние 24 часа — -9, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 15.64%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 2 688 просмотров. В течение первых суток публикация набирает 0 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 24.
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 17 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.

17 181

Подписчики

-924 часа

-317 дней

-15630 день

2 688

Просмотры поста

Нет данных24 часа

Нет данных48 часов

15.64%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

17 181

Hinton our guest in Pavia (remotely) 💚😈

17 181

🔥BoxerNet: SOTA 2D->3D BBs🔥 👉Boxer by #META: transformer-based network to lift 2D BB proposals into 3D, followed by multi-view fusion and geometric filtering to produce globally consistent de-duplicated 3DBBs in metric world space. Repo under A-NC 4.0 International💙 👉Review https://t.ly/mlmV1 👉Paper https://arxiv.org/pdf/2604.05212 👉Project facebookresearch.github.io/boxer/ 👉Repo github.com/facebookresearch/boxer

17 181

🔥Vanast: VTON w/ Human Animation🔥 👉SNU unveils a novel unified framework that generates garment-transferred human animation videos directly from a single human/garment images, and pose guidance clip. Repo announced💙 👉Review https://t.ly/c0t79 👉Paper arxiv.org/pdf/2604.04934 👉Project hyunsoocha.github.io/vanast/ 👉Repo github.com/snuvclab/vanast

17 181

🍎Video Object Deletion🍎 👉Void by Netflix is a novel video object removal framework designed to perform physically-plausible inpainting in very complex scenarios. Repo under Apache 2.0💙 👉Review https://t.ly/cMVny 👉Paper https://arxiv.org/pdf/2604.02296 👉Project https://void-model.github.io/ 👉Repo https://github.com/Netflix/void-model

17 181

If you have to invest TODAY 1B$ on a frontier tech for the next decade, would you invest in space, agentic, quantum or frugal GPUs? Vote here: https://t.ly/hSx6i

17 181

🪬Camera Raw Image Generation🪬 👉RawGen by #Samsung is a generative approach that learns the complex distribution of raw sensor data directly, enabling high-fidelity generation from either text descriptions or standard sRGB images across arbitrary camera sensors. Linear raw image once, then apply any ISP operation. Repo announced💙 👉Review https://t.ly/_QVKP 👉Paper https://arxiv.org/pdf/2604.00093 👉Project https://dy112.github.io/rawgen-page/ 👉Repo TBA

17 181

🌵SOTA Training-Free In-Context Segmentation🌵 👉INSID3 is the new SOTA, training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. Repo under Apache 2.0💙 👉Review https://t.ly/NVWHN 👉Paper https://arxiv.org/pdf/2603.28480 👉Project https://visinf.github.io/INSID3/ 👉Repo https://github.com/visinf/INSID3

17 181

👌HandX: Scaling Hands Motion👌 👉 HandX is a unified foundation spanning data, annotation, and evaluation: novel large-scale dataset of bimanual & dexterous motions with fine-grained textual. Around 6M frames. Repo available💙 👉Review https://t.ly/1nGxw 👉Paper https://arxiv.org/pdf/2603.28766 👉Project https://handx-project.github.io/ 👉Repo github.com/handx-project/HandX

17 181

💥 GaussianGPT 3D GSC💥 👉From TUM, GaussianGPT: transformer-based 3D Gaussians generation via next-token prediction -> full 3D complex indoor scene. Repo announced💙 👉Review https://t.ly/bj-lL 👉Paper https://arxiv.org/pdf/2603.26661 👉Project https://nicolasvonluetzow.github.io/GaussianGPT/ 👉Repo TBA

17 181

🐍Pose-Appearance-Motion for HOI🐍 👉PAM is a novel Pose–Appearance–Motion Engine for controllable Hand–Object Interaction SOTA video generation. Repo/models available💙 👉Review 👉Paper arxiv.org/pdf/2603.22193 👉Project gasaiyu.github.io/PAM.github.io/ 👉Repo https://github.com/GasaiYU/PAM

17 181

🦪OccAny: Universal 3D Occupancy🦪 👉OccAny by Valeo is a novel unified framework for generalized unconstrained urban 3D occupancy prediction. Repo under Apache 2.0💙 👉Review 👉Paper https://arxiv.org/pdf/2603.23502 👉Project https://valeoai.github.io/OccAny/ 👉Repo https://github.com/valeoai/OccAny

17 181

🍓Material-Aware Grouping🍓 👉Material Magic Wand (Adobe) is a tool for material-aware grouping of parts in untextured 3D meshes. Given one selected part, it automatically retrieves the other parts in the same shape by its material. Repo announced💙 👉Review https://t.ly/q00SU 👉Paper https://arxiv.org/pdf/2603.17370 👉Project umangi-jain.github.io/material-magic-wand/ 👉Repo TBA

17 181

🍧10,000× faster SAM-3D🍧 👉Fast SAM 3D Body achieves up to 10.9× speedup, over 10,000× faster MHR-to-SMPL conversion -> real-time humanoid control from RGB. Repo available💙 👉Review https://t.ly/uHx84 👉Paper https://arxiv.org/pdf/2603.15603 👉Project yangtiming.github.io/Fast-SAM-3D-Body-Page/ 👉Repo https://github.com/yangtiming/Fast-SAM-3D-Body

17 181

🤖Physically-Plausible Human🤖 👉PhysMoDPO is a novel direct preference optimization framework for humanoid motion generation. Repo under MIT💙 👉Review https://t.ly/clf8w 👉Paper https://arxiv.org/pdf/2603.13228 👉Project https://mael-zys.github.io/PhysMoDPO/ 👉Repo https://github.com/Mael-zys/PhysMoDPO

17 181

🌈 New SOTA Video Depth 🌈 👉DVD is the new Video Depth Estimation SOTA with full training suite available under Apache2.0💙 👉Review https://t.ly/gpCkG 👉Paper https://arxiv.org/pdf/2603.12250 👉Project https://dvd-project.github.io/ 👉Repo github.com/EnVision-Research/DVD

17 181

☄️OmniStream: Perceive-Reconstruct-Act ☄️ 👉Novel unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. Repo/Models announced💙 👉Review https://t.ly/_zZMO 👉Paper arxiv.org/pdf/2603.12265 👉Project go2heart.github.io/omnistream/ 👉Repo github.com/Go2Heart/OmniStream

17 181

🍓Surface Light Tokenizer🍓 👉Apple unveils LITO a novel latent flow matching model enables HQ image-to-3D. Latent representation that encodes a surface light field into a compact set of latent vectors. Impressive results but no code🥲 👉Review https://t.ly/xcWNe 👉Paper https://lnkd.in/dYHwY4YX 👉Project https://lnkd.in/dtJT8bXy

17 181

🔥Holistic 3D Spatial Intelligence🔥 👉Holi-Spatial is the first fully automated pipeline capable of converting raw video streams into holistic 3D spatial annotations without human intervention. Code/Data announced💙 👉Review https://t.ly/PDpr9 👉Paper https://lnkd.in/dTbMuZCm 👉Project https://lnkd.in/d66CYB4q 👉Repo https://lnkd.in/dAGzShXj

17 181

📊Real-Time Scene Graph📊 👉REACT++ by Umea University is the new state-of-the-art model for real-time SGG: 20% faster with a gain of 10% in relation prediction accuracy on average. Code under MIT💙 👉Review https://t.ly/c12VX 👉Paper https://arxiv.org/pdf/2603.06386 👉Repo https://github.com/Maelic/SGG-Benchmark

17 181

🎪SOTA Arbitrary Tracking🎪 👉TAPFormer is the novel SOTA transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-freq point tracking. Repo & Dataset under MIT💙 👉Review https://t.ly/-q4wm 👉Paper https://arxiv.org/pdf/2603.04989 👉Project http://tapformer.github.io/ 👉Repo https://github.com/ljx1002/TAPFormer