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 7 543 en la categoría Tecnologías y Aplicaciones y el puesto 2 144 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 26 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 250, y en las últimas 24 horas de 1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 17.43%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.04% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 008 visualizaciones. En el primer día suele acumular 1 216 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 27 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
+124 horas
+97 días
+25030 días
Archivo de publicaciones
☔ #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/
🐈 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
🧯 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
💡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
🎪 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
🃏 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
🔥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
🍡 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
🛣️ 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
🪩 DISCO: Human Dance Generation 🪩
👉NTU (+ #Microsoft) unveils DISCO: a big step towards the Human Dance Generation.
😎Review https://t.ly/cNGX
😎Paper arxiv.org/pdf/2307.00040.pdf
😎Project disco-dance.github.io/
😎Code github.com/Wangt-CN/DisCo
🔮 SAM-PT: Segment Anything + Tracking 🔮
👉SAM-PT is the first method to utilize sparse point propagation for Video Object Segmentation (VOS).
😎Review https://t.ly/QLMG
😎Paper arxiv.org/pdf/2307.01197.pdf
😎Project www.vis.xyz/pub/sam-pt/
😎Code github.com/SysCV/sam-pt
🔥🔥 Source Code IS OUT! 🔥🔥
More: https://t.ly/ZddLl
🍥 PanoHead: 3D Full-Head Synthesis 🍥
👉#ByteDance (+UW-M) unveils PanoHead: 360◦ view-consistent portraits from a single-view image
😎Review https://t.ly/MrLNR
😎Paper arxiv.org/pdf/2303.13071.pdf
😎Project sizhean.github.io/panohead
😎Code github.com/sizhean/panohead
🚔 Fooling Neural Forensic Classifiers 🚔
👉Adversarial faces able to fool the forensic classifiers, while remaining undetectable by humans
😎Review https://t.ly/33Cc
😎Paper arxiv.org/pdf/2306.13091.pdf
😎Project koushiksrivats.github.io/face_attribute_attack
😎Code github.com/koushiksrivats/face_attribute_attack
🦷 Few-Shot Geometry-Aware Keypoints 🦷
👉UBC (+Flawless AI) unveils the new SOTA in semantic keypoints localization. Suitable for faces, animals, cars, mouth, teeth & more
😎Review https://t.ly/-0qN
😎Paper arxiv.org/pdf/2303.17216.pdf
😎Project xingzhehe.github.io/FewShot3DKP/
🫣 Text-Guided Adversarial Makeup 🫣
👉Novel facial privacy protection via adversarial latent codes. Makeup vs Face Recognition.
😎Review https://t.ly/pBCP
😎Paper arxiv.org/pdf/2306.10008.pdf
😎Code github.com/fahadshamshad/Clip2Protect
🧿 NeRF-Supervised Deep Stereo 🧿
👉A novel pioneering pipeline for training deep stereo networks WITH NO ground-truth
😎Review https://t.ly/c7j-
😎Project nerfstereo.github.io/
😎Dataset https://amsacta.unibo.it/id/eprint/7218/
😎Code github.com/fabiotosi92/NeRF-Supervised-Deep-Stereo
😎Paper https://openaccess.thecvf.com/content/CVPR2023/papers/Tosi_NeRF-Supervised_Deep_Stereo_CVPR_2023_paper.pdf
👁️ Scene Five: Through Her Eyes 👁️
👉 #3D scene reconstruction of what a person is observing using only the reflections of their eyes
😎Review https://t.ly/Krvw
😎Paper arxiv.org/pdf/2306.09348.pdf
😎Project https://world-from-eyes.github.io/
🌈 Track Everything Everywhere 🌈
👉#Google unveils OmniMotion: full-length motion tracking for every pixel in every frame of video.
😎Review https://t.ly/Krvw
😎Paper arxiv.org/pdf/2306.05422.pdf
😎Project omnimotion.github.io/
😎Demo omnimotion.github.io/#interactive_demo
😎Code github.com/qianqianwang68/omnimotion
🏸 Segment Anything in HQ 🏸
👉HQ-SAM: SAM with the ability to accurately segment objects, maintaining promptable design, efficiency, zero-shot generalizability
😎Report https://t.ly/GxX5B
😎Paper arxiv.org/pdf/2306.01567.pdf
😎Models github.com/SysCV/SAM-HQ
