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

17 157

🫠 X-Portrait 2: SOTA(?) Portrait Animation 🫠 👉ByteDance unveils a preview of X-Portrait2, the new SOTA expression encoder model that implicitly encodes every minuscule expressions from the input by training it on large-scale datasets. Impressive results but no paper & code announced. 👉Review https://t.ly/8Owh9 [UPDATE] 👉Paper ? 👉Project byteaigc.github.io/X-Portrait2/ 👉Repo ?

17 157

🧠 Single Neuron Reconstruction 🧠 👉SIAT unveils NeuroFly, a framework for large-scale single neuron reconstruction. Formulating neuron reconstruction task as a 3-stage streamlined workflow: automatic segmentation - connection - manual proofreading. Bridging computer vision and neuroscience 💙 👉Review https://t.ly/Y5Xu0 👉Paper https://arxiv.org/pdf/2411.04715 👉Repo github.com/beanli161514/neurofly

17 157

💪 Muscles in Time Dataset 💪 👉Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. MinT contains 9+ hours of simulation data covering 227 subjects and 402 simulated muscle strands. Code & Dataset available soon 💙 👉Review https://t.ly/108g6 👉Paper arxiv.org/pdf/2411.00128 👉Project davidschneider.ai/mint 👉Code github.com/simplexsigil/MusclesInTime

17 157

🏣 CityGaussianV2: Large-Scale City 🏣 👉A novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency: 10x compression, 25% faster & -50% memory! Source code released💙 👉Review https://t.ly/Xgn59 👉Paper arxiv.org/pdf/2411.00771 👉Project dekuliutesla.github.io/CityGaussianV2/ 👉Code github.com/DekuLiuTesla/CityGaussian

17 157

☀️ Universal Relightable Avatars ☀️ 👉#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results! 👉Review https://t.ly/U-ESX 👉Paper arxiv.org/pdf/2410.24223 👉Project junxuan-li.github.io/urgca-website

17 157

🍜 REM: Segment What You Describe 🍜 👉REM is a framework for segmenting concepts in video that can be described via LLM. Suitable for rare & non-object dynamic concepts, such as waves, smoke, etc. Code & Data announced 💙 👉Review https://t.ly/OyVtV 👉Paper arxiv.org/pdf/2410.23287 👉Project https://miccooper9.github.io/projects/ReferEverything/

17 157

🔥🔥 The code is out 🔥🔥 👉Code https://github.com/HaixinShi/fmov_pose

17 157

🔥 D-FINE: new SOTA Detector 🔥 👉D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR model. New SOTA on MS COCO with additional data. Code & models available 💙 👉Review https://t.ly/aw9fN 👉Paper https://arxiv.org/pdf/2410.13842 👉Code https://github.com/Peterande/D-FINE

17 157

🫐 Blendify: #Python + Blender 🫐 👉Lightweight Python framework that provides a high-level API for creating & rendering scenes with #Blender. It simplifies data augmentation & synthesis. Source Code released💙 👉Review https://t.ly/l0crA 👉Paper https://arxiv.org/pdf/2410.17858 👉Code https://virtualhumans.mpi-inf.mpg.de/blendify/

17 157

⛈️ SMITE: SEGMENT IN TIME ⛈️ 👉SFU unveil SMITE: a novel AI that -with only one or few segmentation references with fine granularity- is able to segment different unseen videos respecting the segmentation references. Dataset & Code (under Apache 2.0) announced 💙 👉Review https://t.ly/w6aWJ 👉Paper arxiv.org/pdf/2410.18538 👉Project segment-me-in-time.github.io/ 👉Code github.com/alimohammadiamirhossein/smite/

17 157

🌻 Plant Camouflage Detection🌻 👉PlantCamo Dataset is the first dataset for plant camouflage detection: 1,250 images with camouflage characteristics. Source Code released 💙 👉Review https://t.ly/pYFX4 👉Paper arxiv.org/pdf/2410.17598 👉Code github.com/yjybuaa/PlantCamo

17 157

🪁 PL2Map: efficient neural 2D-3D 🪁 👉PL2Map is a novel neural network tailored for efficient representation of complex point & line maps. A natural representation of 2D-3D correspondences 👉Review https://t.ly/D-bVD 👉Paper arxiv.org/pdf/2402.18011 👉Project https://thpjp.github.io/pl2map 👉Code https://github.com/ais-lab/pl2map

17 157

🧿 Look Ma, no markers 🧿 👉#Microsoft unveils the first technique for marker-free, HQ reconstruction of COMPLETE human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware. Impressive results! Repo for training & Dataset released💙 👉Review https://t.ly/5fN0g 👉Paper arxiv.org/pdf/2410.11520 👉Project microsoft.github.io/SynthMoCap/ 👉Repo github.com/microsoft/SynthMoCap

17 157

🔥BitNet: code of 1-bit LLM is out 🔥 👉BitNet by #Microsoft, announced in late 2023, is a 1-bit Transformer architecture designed for LLMs. BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Source Code just released a few hours ago 💙 👉Review https://t.ly/3G2LA 👉Paper arxiv.org/pdf/2310.11453 👉Code https://lnkd.in/duPADJVb

17 157

☀️ GS + Depth = SOTA ☀️ 👉ETH unveils DepthSplat, the new SOTA in depth estimation and novel view synthesis tasks. The key feature is the cross-task interactions between Gaussian Splatting & depth estimation. Source Code to be released in a few days💙 👉Review https://t.ly/87HuH 👉Paper arxiv.org/abs/2410.13862 👉Project haofeixu.github.io/depthsplat/ 👉Code github.com/cvg/depthsplat

17 157

🦠 Neural Metamorphosis 🦠 👉NU Singapore unveils NeuMeta to transform neural nets by allowing a single model to adapt on the fly to different sizes, generating the right weights when needed. 👉Review https://t.ly/DJab3 👉Paper arxiv.org/pdf/2410.11878 👉Project adamdad.github.io/neumeta 👉Code github.com/Adamdad/neumeta

17 157

🔥 CoTracker3 by #META is out! 🔥 👉#Meta (+VGG Oxford) unveils CoTracker3, a new tracker that outperforms the previous SoTA by a large margin using only the 0.1% of the training data 🤯🤯🤯 👉Review https://t.ly/TcRIv 👉Paper arxiv.org/pdf/2410.11831 👉Project cotracker3.github.io/ 👉Code github.com/facebookresearch/co-tracker

17 157

🪞Robo-Emulation via Video Imitation🪞 👉OKAMI (UT & #Nvidia) is a novel foundation method that generates a manipulation plan from a single RGB-D video and derives a policy for execution. 👉Review https://t.ly/_N29- 👉Paper arxiv.org/pdf/2410.11792 👉Project https://lnkd.in/d6bHF_-s

17 157

🔥 DEPTH ANY VIDEO is out! 🔥 👉DAV is a novel foundation model for image/video depth estimation.The new SOTA for accuracy & consistency, up to 150 FPS! 👉Review https://t.ly/CjSz2 👉Paper arxiv.org/pdf/2410.10815 👉Project depthanyvideo.github.io/ 👉Code github.com/Nightmare-n/DepthAnyVideo