AI with Papers - Artificial Intelligence & Deep Learning

Ir al canal en Telegram

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 240 Tecnologías y Aplicaciones7 726...

📈 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 17 154 suscriptores, ocupando la posición 7 726 en la categoría Tecnologías y Aplicaciones y el puesto 2 240 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 154 suscriptores.

Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -166, y en las últimas 24 horas de -6, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 23.63%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 6.86% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 4 057 visualizaciones. En el primer día suele acumular 1 177 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 26.
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 22 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.

17 154

Suscriptores

-624 horas

-277 días

-16630 días

4 057

Visitas de la publicación

~ 1 17724 horas

~ 1 35348 horas

23.63%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 154

📫 MeshPose: DensePose + HMR 📫 👉MeshPose: novel approach to jointly tackle DensePose and Human Mesh Reconstruction in a while. A natural fit for #AR applications requiring real-time mobile inference. 👉Review https://t.ly/a-5uN 👉Paper https://arxiv.org/pdf/2406.10180 👉Project https://meshpose.github.io/

17 154

🎹 PianoMotion10M for gen-hands 🎹 👉PianoMotion10M: 116 hours of piano playing videos from a bird’s-eye view with 10M+ annotated hand poses. A big contributions in hand motion generation. Code & Dataset released💙 👉Review https://t.ly/_pKKz 👉Paper arxiv.org/pdf/2406.09326 👉Code https://lnkd.in/dcBP6nvm 👉Project https://lnkd.in/d_YqZk8x 👉Dataset https://lnkd.in/dUPyfNDA

17 154

🍉 MASA: MOT Anything By SAM 🍉 👉MASA: Matching Anything by Segmenting Anything pipeline to learn object-level associations from unlabeled images of any domain. An universal instance appearance model for matching any objects in any domain. Source code in June 💙 👉Review https://t.ly/pKdEV 👉Paper https://lnkd.in/dnjuT7xm 👉Project https://lnkd.in/dYbWzG4E 👉Code https://lnkd.in/dr5BJCXm

17 154

👑 Kling AI vs. OpenAI Sora 👑 👉Kling: the ultimate Chinese text-to-video model - rival to #OpenAI’s Sora. No papers or tech info to check, but stunning results from the official site. 👉Review https://t.ly/870DQ 👉Paper ??? 👉Project https://kling.kuaishou.com/

17 154

👗 SOTA Multi-Garment VTOn Editing 👗 👉#Google (+UWA) unveils M&M VTO, novel mix 'n' match virtual try-on that takes as input multiple garment images, text description for garment layout and an image of a person. It's the new SOTA both qualitatively and quantitatively. Impressive results! 👉Review https://t.ly/66mLN 👉Paper arxiv.org/pdf/2406.04542 👉Project https://mmvto.github.io

17 154

🧊 Universal 6D Pose/Tracking 🧊 👉Omni6DPose is a novel dataset for 6D Object Pose with 1.5M+ annotations. Extra: GenPose++, the novel SOTA in category-level 6D estimation/tracking thanks to two pivotal improvements. 👉Review https://t.ly/Ywgl1 👉Paper arxiv.org/pdf/2406.04316 👉Project https://lnkd.in/dHBvenhX 👉Lib https://lnkd.in/d8Yc-KFh

17 154

🚙 UA-Track: Uncertainty-Aware MOT🚙 👉UA-Track: novel Uncertainty-Aware 3D MOT framework which tackles the uncertainty problem from multiple aspects. Code announced, not released yet. 👉Review https://t.ly/RmVSV 👉Paper https://arxiv.org/pdf/2406.02147 👉Project https://liautoad.github.io/ua-track-website

17 154

📞FacET: VideoCall Change Your Expression📞 👉Columbia University unveils FacET: discovering behavioral differences between conversing face-to-face (F2F) and on video-calls (VCs). 👉Review https://t.ly/qsQmt 👉Paper arxiv.org/pdf/2406.00955 👉Project facet.cs.columbia.edu/ 👉Repo (empty) github.com/stellargo/facet

17 154

👹👹 AI and the Everything in the Whole Wide World Benchmark 👹👹 👉Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”. 👉Review https://t.ly/isdxM 👉Paper https://lnkd.in/dFraieZS 👉News https://lnkd.in/da-7PnVT

17 154

👹👹👹👹 Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”.

17 154

🐳 MultiPly: in-the-wild Multi-People 🐳 👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙 👉Review https://t.ly/_xjk_ 👉Paper arxiv.org/pdf/2406.01595 👉Project eth-ait.github.io/MultiPly 👉Repo github.com/eth-ait/MultiPly

17 154

🐳MultiPly: in-the-wild Multi-People from Mono🐳 👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙 #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2406.01595 👉Project https://eth-ait.github.io/MultiPly/ 👉Repo https://github.com/eth-ait/MultiPly

17 154

🎭New 2D Landmarks SOTA🎭 👉Flawless AI unveils FaceLift, a novel semi-supervised approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment, with no need for 3D landmark datasets. No code announced🥹 👉Review https://t.ly/lew9a 👉Paper arxiv.org/pdf/2405.19646 👉Project davidcferman.github.io/FaceLift

17 154

🧤 Transformer-based 4D Hands 🧤 👉4DHands is a novel and robust approach to recovering interactive hand meshes and their relative movement from monocular inputs. Authors: Beijing Normal University, Tsinghua & #Lenovo. No code announced yet 😢 👉Review https://t.ly/wvG-l 👉Paper arxiv.org/pdf/2405.20330 👉Project 4dhands.github.io/

17 154

🪰 Dynamic Gaussian Fusion via 4D Motion Scaffolds 🪰 👉MoSca is a novel 4D Motion Scaffolds to reconstruct/synthesize novel views of dynamic scenes from monocular videos in the wild! 👉Review https://t.ly/nSdEL 👉Paper arxiv.org/pdf/2405.17421 👉Code github.com/JiahuiLei/MoSca 👉Project https://lnkd.in/dkjMVcqZ

17 154

🦓 Z.S. Diffusive Segmentation 🦓 👉KAUST (+MPI) announced the first zero-shot approach for Video Semantic Segmentation (VSS) based on pre-trained diffusion models. Source Code released under MIT💙 👉Review https://t.ly/v_64K 👉Paper arxiv.org/pdf/2405.16947 👉Project https://lnkd.in/dcSt4dQx 👉Code https://lnkd.in/dcZfM8F3

17 154

⛈️Unsupervised Neuromorphic Motion⛈️⛈️ 👉The Western Sydney University unveils a novel unsupervised event-based motion segmentation algorithm, employing the #Prophesee Gen4 HD event camera. 👉Review https://t.ly/UZzIZ 👉Paper https://arxiv.org/pdf/2405.15209 👉Project https://samiarja.github.io/evairborne/ 👉Repo (empty) https://github.com/samiarja/ev/_deep/_motion_segmentation

17 154

🔥 YOLOv10 Object Detector is out 🔥 👉YOLOv10: novel real-time end-to-end object detection. Code released under GNU GPL v3.0💙 👉Review https://shorturl.at/ZIHBh 👉Paper arxiv.org/pdf/2405.14458 👉Code https://github.com/THU-MIG/yolov10/

17 154

🍀 OmniGlue: Foundation Matcher 🍀 👉#Google OmniGlue from #CVPR24: the first learnable image matcher powered by foundation models. Impressive out-of-domain results! 👉Review https://t.ly/ezaIc 👉Paper https://arxiv.org/pdf/2405.12979 👉Project hwjiang1510.github.io/OmniGlue/ 👉Code https://github.com/google-research/omniglue/

17 154

👚 ViViD: Diffusion Virtual Try-ON 👚 👉ViViD is a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Code announced, not released yet😢 👉Review https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2405.11794 👉Repo https://lnkd.in/dT4_bzPw 👉Project https://lnkd.in/dCK5ug4v