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 241 Tecnologías y Aplicaciones7 723...

📈 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 17 142 suscriptores, ocupando la posición 7 723 en la categoría Tecnologías y Aplicaciones y el puesto 2 241 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 142 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -190, y en las últimas 24 horas de -2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 25.09%. 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 4 302 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 24 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.

17 142

Suscriptores

-224 horas

-367 días

-19030 días

4 302

Visitas de la publicación

~ 1 17724 horas

~ 1 35348 horas

25.09%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 137

🦀 simPLE: learning to grasp only with CAD 🦀 👉simPLE learns to pick, regrasp & place objects precisely, given only the object CAD model and no prior experience 😎Review https://t.ly/ab5pA 😎Paper arxiv.org/pdf/2307.13133.pdf 😎Project mcube.mit.edu/research/simPLE.html

17 137

🥬 Generative AI’s Next Frontiers 🥬 👉Hair simulation, 2D->3D animation, and much more. ~20 papers from #NVIDIA accepted into #SIGGRAPH2023 😎 Review https://t.ly/wgGin

17 137

🛵ALPR via CTS-Matching 🛵 👉UIT unveils a neural approach (#YOLO5 + tracking + rotation) to improve the license plate recognition accuracy 😎Review https://t.ly/VP4BP 😎Paper arxiv.org/pdf/2307.11336.pdf 😎Code github.com/chequanghuy/Character-Time-series-Matching

17 137

🪛 CAD-based Object Segmentation 🪛 👉 A novel three-stage approach to segment unseen objects in RGB images using their CAD models 😎Review https://t.ly/RtHLN 😎Paper arxiv.org/pdf/2307.11067.pdf 😎Code https://github.com/nv-nguyen/cnos

17 137

🪤 PAPR: Proximity Attention Point Render 🪤 👉PAPR: fast point-based scene representation with differentiable renderer approach 😎Review https://t.ly/yoI0g 😎Paper arxiv.org/pdf/2307.11086.pdf 😎Project https://zvict.github.io/papr

17 137

🪤 PAPR: Proximity Attention Point Render 🪤 👉PAPR: fast point-based scene representation with differentiable renderer approach

17 137

💪 Muscles in Action with #AI 💪 👉Muscles in Action (MIA): learn to incorporate muscle activity into human motion representations 😎Review https://t.ly/hUKub 😎Paper arxiv.org/pdf/2212.02978.pdf 😎Project musclesinaction.cs.columbia.edu

17 137

👩‍🦰 Ultra-Realistic Neural Hair 👩‍🦰 👉A novel method to reconstruct the hair geometry at a strand level from monocular video or multi-view images 😎Review https://t.ly/6xZyp 😎Paper arxiv.org/pdf/2306.05872.pdf 😎Project samsunglabs.github.io/NeuralHaircut 😎Code github.com/SamsungLabs/NeuralHaircut

17 137

🪟 META's Ultra-Realistic Data for #AR🪟 👉Aria Digital Twin: egocentric dataset for object detection/tracking, reconstruction/understanding, S2R learning, human pose prediction and more 😎Review https://t.ly/MRPt1 😎Paper arxiv.org/pdf/2306.06362.pdf 😎Project www.projectaria.com/datasets/adt/ 😎Code github.com/facebookresearch/projectaria_tools

17 137

🍉 AltFreezing: new SOTA in detecting fake-faces 🍉 👉#Microsoft unveils AltFreezing: spatial/temporal artifacts in one model for more general face forgery detection 😎Review https://t.ly/mkIKX 😎Paper https://t.ly/z4KnJ 😎Code github.com/ZhendongWang6/AltFreezing

17 137

🦙 Llama-2: the Open-Source "#chatgpt"🦙 👉GenAI, #Meta unveils Llama-2: a collection of LLMs ranging in scale 7-70B parameters. Challenging with #chatgpt, but open. 😎Review https://t.ly/bLJgP 😎Paper https://t.ly/AOXru 😎Project https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/

17 137

☔ #SelfDriving? It's all about weather! ☔ 👉Novel self-supervised MDE method to handle adverse weather in real-world autonomous driving 😎Review https://t.ly/tcLQW 😎Paper arxiv.org/pdf/2307.08357.pdf 😎Project kieran514.github.io/Robust-Depth-Project/

17 137

🐈 Gen-AI as representation learner 🐈 👉DreamTeacher: novel self-supervised feats. representation learning framework that utilizes gen-nets for pre-training downstream image backbones 😎Review https://t.ly/RL8iG 😎Paper arxiv.org/pdf/2307.07487.pdf 😎Project research.nvidia.com/labs/toronto-ai/DreamTeacher

17 137

🧯 Neural Focal Modulation for VAR 🧯 👉Video-FocalNet is a novel architecture for video recognition that models both local and global context 😎Review https://t.ly/rF_fk 😎Paper arxiv.org/pdf/2307.06947.pdf 😎Project talalwasim.github.io/Video-FocalNets 😎Code github.com/TalalWasim/Video-FocalNets

17 137

💡DATID-3D: Diffusive Text-to-3D Generation💡 👉 A novel domain adaptation method for 3D via text-to-image diffusion. 🤗-Demo available! 😎Review https://t.ly/ecBvM 😎Paper arxiv.org/pdf/2211.16374.pdf 😎Project gwang-kim.github.io/datid_3d/ 😎Code github.com/gwang-kim/DATID-3D 🤗Demo huggingface.co/spaces/gwang-kim/DATID-3D 😎Colab colab.research.google.com/drive/1e9NSVB7x_hjz-nr4K0jO4rfTXILnNGtA?usp=sharing

17 137

🎪 Extreme Human Pose Estimation 🎪 👉RePoGen: novel synthetic data generator of extreme/realistic poses of humans 😎Review https://t.ly/ecBvM 😎Paper arxiv.org/pdf/2307.06737.pdf 😎Project mirapurkrabek.github.io/RePoGen-paper 😎Code github.com/MiraPurkrabek/RePoGen

17 137

🃏 Deepfake via casual self-scan 🃏 👉TAU presents a novel approach to reenact an ID using only a casual self-scan 😎Review https://t.ly/9T8Wi 😎Paper arxiv.org/pdf/2307.06307.pdf 😎Project arielazary.github.io/PGR

17 137

🔥o-TTT: Test-Time Training on fire 🔥 👉Extending the TTT to the streaming setting. Suitable for Panoptic, Instance & Colorization. 😎Review https://t.ly/eZYA 😎Paper arxiv.org/pdf/2307.05014.pdf 😎Project https://video-ttt.github.io/ 😎Code github.com/renwang435/video-ttt-release

17 137

🍡 Text2Cinemagraphs: Cinemagraph from text 🍡 👉CMU (+ #Snap) unveils a fully automated method for creating cinemagraphs from text descriptions 😎Review https://t.ly/BwZs6 😎Paper arxiv.org/pdf/2307.03190.pdf 😎Project text2cinemagraph.github.io/website/ 😎Code github.com/text2cinemagraph/text2cinemagraph

17 137

🛣️ STAR.: 3D-tracking w/ attention paradigm 🛣️ 👉#Mercedes STAR: e2e 3D object tracking that follows the tracking-by-attention paradigm 😎Review https://t.ly/JoGj 😎Paper arxiv.org/pdf/2306.17602.pdf 😎Project simondoll.github.io/publications/star_track