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
Mostrar más📈 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 17 261 suscriptores, ocupando la posición 7 540 en la categoría Tecnologías y Aplicaciones y el puesto 2 147 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 261 suscriptores.
Según los últimos datos del 27 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 242, 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 16.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.10% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 923 visualizaciones. En el primer día suele acumular 1 225 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 16.
- 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 28 octubre, 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.
17 261
Suscriptores
-224 horas
+67 días
+24230 días
Archivo de publicaciones
🐓 DREAMIX: General Diffusion Video Editors 🐓
👉#Google unveils the first diffusion-based method able to perform text-based motion/appearance editing of general videos
😎Review https://bit.ly/3I3Hq6B
😎Paper arxiv.org/pdf/2302.01329.pdf
😎Project dreamix-video-editing.github.io/
💧FLOW360: 360° Neural Optical Flow💧
👉 IIT unveils the first perceptually realistic 360° video benchmark dataset + SLOF method for OF tracking
😎Review https://bit.ly/3wMZZoX
😎Paper arxiv.org/pdf/2301.11880.pdf
😎Project https://siamlof.github.io
🛋️🛋️ 100% Accurated #3D Labeling 🛋️🛋️
👉#Amazon unveils a novel tool for fine-grained 3D part labeling. Up to 100% accuracy! Paper (only😢)
😎Review https://bit.ly/3kYpQHQ
😎Paper https://arxiv.org/pdf/2301.10460.pdf
⭐ Mono-STAR: Unified Tracking/Reconstruction ⭐
👉Real-time 3D unified framework for semantic fusion, tracking, non-rigid deformation, and topological changes
😎Review https://bit.ly/3Dxvxmx
😎Paper arxiv.org/pdf/2301.13244.pdf
😎Project github.com/changhaonan/Mono-STAR-demo
🚛 Text-driven Video Neural Editing 🚛
👉A novel text-guided video editing with both appearance/shape
😎Review https://bit.ly/3YcfMJO
😎Paper arxiv.org/pdf/2301.13173.pdf
😎Project text-video-edit.github.io/
🎷Audio-Visual Semantic Segmentation🎷
👉A novel problem in #AI: pixel-level segmentation of objects that produce sound in the image frame
😎Review https://bit.ly/3wFY6dw
😎Paper arxiv.org/pdf/2301.13190.pdf
😎Project opennlplab.github.io/AVSBench
😎Code github.com/OpenNLPLab/AVSBench
🐦 PhyCV: Physics-inspired Computer Vision 🐦
👉From UCLA, the first Physics-inspired Computer Vision Library
😎Review https://bit.ly/3HEWozI
😎Code github.com/JalaliLabUCLA/phycv
😎Project photonics.ucla.edu/2022/05/12/jalali-lab-open-sources-phycv-a-physics-inspired-computer-vision-library/
😍 CLIP/GPT3-driven Affective Faces 😍
👉Columbia unveils a novel framework for facial expressions retrieval given the context of the speaker
😎Review https://bit.ly/3HERna0
😎Paper arxiv.org/pdf/2301.10939.pdf
😎Project realtalk.cs.columbia.edu
😎Code github.com/scottgeng00/realtalk
🔥CutLER: Unsupervised Segmentation 🔥
👉Novel paper by #META on detection & instance segmentation without human annotations
😎Review https://bit.ly/3DlFiUG
😎Paper arxiv.org/pdf/2301.11320.pdf
😎Code github.com/facebookresearch/CutLER
😎Project people.eecs.berkeley.edu/~xdwang/projects/CutLER
🐕 MAV3D: #3D Video from Text 🐕
👉#META unveils a novel #AI for generating #3D dynamic videos from text
😎Review https://bit.ly/3WPRAvK
😎Paper arxiv.org/pdf/2301.11280.pdf
😎Project make-a-video3d.github.io
🧠 SERENA: LLM for Mental Health Support 🧠
👉Interactive #AI (in "#chatgpt" style) designed for mental health counseling
😎Review https://bit.ly/3wtbW37
😎Paper arxiv.org/pdf/2301.09412.pdf
😎Project https://serena.chat/
🧠 SERENA: LLM for Mental Health Support 🧠
👉Interactive #AI (in "#chatgpt" style) designed for mental health counseling
😎Review https://bit.ly/3wtbW37
😎Paper arxiv.org/pdf/2301.09412.pdf
😎Project https://serena.chat/
♿ Detecting Vulnerable Pedestrian ♿
👉 BGSU opens a novel pedestrian dataset for vulnerable people
😎Review https://bit.ly/3JjVmu2
😎Paper arxiv.org/pdf/2212.06218.pdf
😎Project github.com/devvansh1997/BGVP
🔥 Neural Tracking via Weighted OF 🔥
👉The new SOTA in planar neural tracking is out. INSANE!
😎Review https://bit.ly/404gcDs
😎Paper arxiv.org/pdf/2301.10057.pdf
😎Code github.com/serycjon/WOFT
😎Project cmp.felk.cvut.cz/~serycjon/WOFT
🪀 NeRF in Time, Space and Appearance 🪀
👉From Berkeley k-planes: a white-box model for radiance fields in arbitrary dimensions
😎Review https://bit.ly/3J8GiiS
😎Paper arxiv.org/pdf/2301.10241.pdf
😎Project sarafridov.github.io/K-Planes/
😎Code github.com/sarafridov/K-Planes
⚡StyleGAN-T: unlocking the Power of GANs⚡
👉#Nvidia (+Tubingen) unveils StyleGAN-T to regain competitiveness to GANs vs. Diffusive Models
😎Review https://bit.ly/3HtKxEA
😎Paper arxiv.org/pdf/2301.09515.pdf
😎Project sites.google.com/view/stylegan-t
😎Code github.com/autonomousvision/stylegan-t
🍄 BTS: Density Fields from Single View 🍄
👉Volumetric scene representation from a single image in challenging conditions
😎Review https://bit.ly/3wjHDvH
😎Paper arxiv.org/pdf/2301.07668.pdf
😎Project fwmb.github.io/bts/
👑 OnePose++: One-Shot Pose 👑
👉Open-source (insane) keypoint-free pose pipeline for #AR
😎Review https://bit.ly/3kF0BdG
😎Paper arxiv.org/pdf/2301.07673.pdf
😎Project zju3dv.github.io/onepose_plus_plus
😎Code github.com/zju3dv/OnePose_Plus_Plus
😎Dataset zjueducn-my.sharepoint.com/personal/12121064_zju_edu_cn/_layouts/15/onedrive.aspx
🏞OmniObject3D: Realistic 3D Dataset 🏞
👉Large-vocabulary #3D dataset for realistic perception, reconstruction & generation
😎Review https://bit.ly/3HlXyjp
😎Paper arxiv.org/pdf/2301.07525.pdf
😎Project omniobject3d.github.io/
🎨 RecolorNeRF: #3D Color Editing 🎨
👉INSANE palette-based color editing of NeRF scenes
😎Review https://bit.ly/3GYjhfR
😎Paper arxiv.org/pdf/2301.07958.pdf
😎Project sites.google.com/view/recolornerf
