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

Malasia2 234 Tecnologías y Aplicaciones7 729...

📈 Análisis del canal de Telegram AI with Papers - Artificial Intelligence & Deep Learning

El canal AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 17 196 suscriptores, ocupando la posición 7 729 en la categoría Tecnologías y Aplicaciones y el puesto 2 234 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 196 suscriptores.

Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -144, y en las últimas 24 horas de -14, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 19.95%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 430 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 19.
Intereses temáticos: El contenido se centra en temas clave como framework, object, dataset, tba, depth.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 15 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

17 196

Suscriptores

-1424 horas

-437 días

-14430 días

3 430

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

19.95%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 196

Hinton our guest in Pavia (remotely) 💚😈

17 196

🔥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

17 196

🔥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

17 196

🍎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

17 196

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

17 196

🪬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

17 196

🌵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

17 196

👌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

17 196

💥 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

17 196

🐍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

17 196

🦪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

17 196

🍓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

17 196

🍧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

17 196

🤖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

17 196

🌈 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

17 196

☄️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

17 196

🍓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

17 196

🔥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

17 196

📊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

17 196

🎪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