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 17 151 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 17 151 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.

17 151

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

17 151

🛸Real-Time Differentiable Tracing🛸 👉 Radiant Foam is a novel scene representation by leveraging the decades-old efficient volumetric mesh ray tracing algorithm (largely overlooked in recent research). Performing like Gaussian Splatting, without the constraints of rasterization. Code announced💙 👉Review https://shorturl.at/26U06 👉Paper https://arxiv.org/pdf/2502.01157 👉Project https://radfoam.github.io/ 👉Repo https://github.com/theialab/radfoam

17 151

🐙MambaGlue: SOTA feats. matching🐙 👉MambaGlue is a hybrid neural network combining the Mamba and the Transformer architectures to match local features. Source Code announced, to be released💙 👉Review https://shorturl.at/LxDG1 👉Paper arxiv.org/pdf/2502.00462 👉Repo https://lnkd.in/dAujfGZQ

17 151

🈯 SOTA 0-Shot Multi-View 🈯 👉MVGD by #TOYOTA is the SOTA method that generates images and scale-consistent depth maps from novel viewpoints given an arbitrary number of posed input views. A novel diffusion-based architecture capable of direct pixel-level generation. Code announced 💙 👉Review https://t.ly/_ecKl 👉Paper arxiv.org/pdf/2501.18804 👉Project mvgd.github.io/ 👉Repo TBA

17 151

💎AI-driven Docs Conversion💎 👉Docling by IBM, is the ALL-in-ONE, open source solution for documents; parsing several types of popular formats into a unified, richly structured representation. Powered by SOTA models for layout (DocLayNet) and table structure (TableFormer), it runs efficiently on low-cost hardware. Code under MIT💙 👉Review https://t.ly/nSCfT 👉Paper https://lnkd.in/dc5Kpc2F 👉Repo https://lnkd.in/d9gvw9bt

17 151

Social feed of everyone is broken because of unnecessary/not required opinions about DeepSeek. Your wish:

Anonymous voting

17 151

🌅 Generative Human Mesh Recovery 🌅 👉GenHMR is a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in 2D-to-3D mapping process. Impressive results but no code announced 🥺 👉Review https://t.ly/Rrzpj 👉Paper https://arxiv.org/pdf/2412.14444 👉Project m-usamasaleem.github.io/publication/GenHMR/GenHMR.html

17 151

☀️ Relightable Full-Body Avatars ☀️ 👉#Meta unveils the first approach ever to jointly model the relightable appearance of the body, face, and hands of drivable avatars. 👉Review https://t.ly/kx9gf 👉Paper arxiv.org/pdf/2501.14726 👉Project neuralbodies.github.io/RFGCA

17 151

🦕[SOTA] Visual Grounding VOS🦕 👉ReferDINO is the first end-to-end approach for adapting foundational visual grounding models to RVOS. Code & models to be released soon💙 👉Review https://t.ly/SDFy9 👉Paper arxiv.org/pdf/2501.14607 👉Project isee-laboratory.github.io/ReferDINO/ 👉Repo github.com/iSEE-Laboratory/ReferDINO

17 151

🎨MatAnyone: Human Matting🎨 👉MatAnyone is a novel approach for human video matting that supports the target assignment. Stable tracking in long videos even with complex/ambiguous BGs. Code & 🤗-Demo announced💙 👉Review https://t.ly/NVXsT 👉Paper arxiv.org/pdf/2501.14677 👉Project pq-yang.github.io/projects/MatAnyone 👉Repo TBA

17 151

🪆SOTA Points Segmentation🪆 👉VGG Oxford unveils a novel loss to segment objects in videos based on their motion and NO other forms of supervision! Training the net using long-term point trajectories as a supervisory signal to complement optical flow. New SOTA! 👉Review https://t.ly/8Bsbt 👉Paper https://arxiv.org/pdf/2501.12392 👉Code https://github.com/karazijal/lrtl 👉Project www.robots.ox.ac.uk/~vgg/research/lrtl/

17 151

🔥 The code of DynOMo is out 🔥 👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input 👉Review https://t.ly/t5pCf 👉Paper https://lnkd.in/dwhzz4_t 👉Repo github.com/dvl-tum/DynOMo 👉Project https://lnkd.in/dMyku2HW

17 151

🔥 The code of DynOMo is out 🔥 👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input. Source code released under BSD 3-Clause💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅TUM, CMU (H/T Jenny Seidenschwarz) & NVIDIA ✅Online 2D/3D point tracking from unposed monocular ✅Tracking-by-reconstruction baseline for online TAP ✅New baseline for online PT with unposed mono-cam hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse hashtag#LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://lnkd.in/dwhzz4_t 👉Repo github.com/dvl-tum/DynOMo 👉Project https://lnkd.in/dMyku2HW

17 151

🦠A-Life with Foundation Models🦠 👉A super team unveils ASAL, a new paradigm for Artificial Life research. A diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia & Neural Cellular Automata. Code under Apache 2.0💙 👉Review https://t.ly/7SZ8A 👉Paper arxiv.org/pdf/2412.17799 👉Project http://pub.sakana.ai/asal/ 👉Repo https://lnkd.in/dP5yxKtw

17 151

🎤EMO2: Audio-Driven Avatar🎤 👉Alibaba previews a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Turn your audio ON. Stunning results but no code 🥺 👉Review https://t.ly/x8slQ 👉Paper arxiv.org/pdf/2501.10687 👉Project humanaigc.github.io/emote-portrait-alive-2/ 👉Repo 🥺

17 151

🧵Time-Aware Pts-Tracking🧵 👉Chrono: feature backbone specifically designed for point tracking with built-in temporal awareness. Long-term temporal context, enabling precise prediction even without the refinements. Code announced💙 👉Review https://t.ly/XAL7G 👉Paper arxiv.orgzpdf/2501.12218 👉Project cvlab-kaist.github.io/Chrono/ 👉Repo github.com/cvlab-kaist/Chrono

17 151

🔥 [SOTA] Long-Video Depth Anything 🔥 👉ByteDance unveils Video Depth Anything: HQ, consistent depth estimation in SUPER-long videos (over several minutes) without sacrificing efficiency. Based on Depth Anything V2 with a novel efficient spatial-temporal head. Repo available under Apache 2.0💙 👉Review https://t.ly/Q4ZZd 👉Paper arxiv.org/pdf/2501.12375 👉Project https://lnkd.in/dKNwJzbM 👉Repo https://lnkd.in/ddfwwpCj

17 151

🌈 #Nvidia Foundation ZS-Stereo 🌈 👉Nvidia unveils FoundationStereo, a foundation model for stereo depth estimation with strong zero-shot generalization. In addition, a large-scale (1M stereo pairs) synthetic training dataset featuring large diversity and high photorealism. Code, model & dataset to be released💙 👉Review https://t.ly/rfBr5 👉Paper arxiv.org/pdf/2501.09898 👉Project nvlabs.github.io/FoundationStereo/ 👉Repo github.com/NVlabs/FoundationStereo/tree/master

17 151

🧽 Diffusion Video Inpainting 🧽 👉#Alibaba unveils a technical report about DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. Code & weights released under Apache💙 👉Review https://t.ly/7rEll 👉Paper arxiv.org/pdf/2501.10018 👉Project lixiaowen-xw.github.io/DiffuEraser-page/ 👉Repo github.com/lixiaowen-xw/DiffuEraser

17 151

🏄‍♀️ GSTAR: Gaussian Surface Tracking 🏄‍♀️ 👉ETH Zurich unveils GSTAR, a novel framework for photo-realistic rendering, surface reconstruction, and 3D tracking for dynamic scenes while handling topology changes. Code announced💙 👉Review https://t.ly/udpMq 👉Paper arxiv.org/pdf/2501.10283 👉Project chengwei-zheng.github.io/GSTAR/ 👉Repo TBA

17 151

🎁Free Book: LLM Foundations🎁 👉A fully free book just released on arXiv to outline the basic concepts of #LLMs and related techniques with a focus on the foundational aspects. ✅Chapter 1: basics of pre-training ✅Chapter 2: gen-models & LLMs ✅Chapter 3: prompting methods ✅Chapter 4: alignment methods 👉If you have any background in ML, along with a certain understanding of stuff like Transformers, this book will be "smooth". However, even without this prior knowledge, it is still perfectly fine because the contents of each chapter are self-contained. 👉Review https://t.ly/9LGCa 👉Book https://lnkd.in/d3VkswZf