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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 17 136 suscriptores, ocupando la posición 7 701 en la categoría Tecnologías y Aplicaciones y el puesto 2 225 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 136 suscriptores.
Según los últimos datos del 25 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -186, y en las últimas 24 horas de 3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 23.94%. 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 0 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 0.
- 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 26 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.
17 136
Suscriptores
+324 horas
-367 días
-18630 días
Archivo de publicaciones
🦑Big Egocentric Dataset by #Meta 🦑
👉Novel dataset to speed-up research on egocentric MR/AI
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅159 sequences, multiple sensors
✅Scenarios: cooking, exercising, etc.
✅‘Desktop Activities’ via multi-view mocap
✅Dataset available upon request
More: https://bit.ly/3QDccVW
🐠Largest Dataset for #autonomousdriving🐠
👉SHIFT: largest synthetic dataset for #selfdrivingcars. Shifts in cloud, rain, fog, time of day, vehicle & pedestrian density🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅4,800+ clips, multi-view sensor suite
✅Semantic/instance, M/stereo depth
✅2D/3D object detection, MOT
✅Optical flow, point cloud registration
✅Visual-Odo, trajectory & human pose
More: https://bit.ly/3HJBUUT
🍅Segmentation with INSANE Occlusions🍅
👉CMU unveils WALT: segmenting in severe occlusion scenarios. Performance over human.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅WALT: Watch & Learn Time-lapse
✅4K/1080p cams on streets over a year
✅Performance over human-supervised
✅Object-occluder-occluded neural layers
✅Source code under MIT license
More: https://bit.ly/3n7pvjO
🔥 #AIwithPapers: we are 2,900+! 🔥
💙💛 Cheers from "Black Metal Lady Gaga" plotted by DallE-mini 💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
✋HaGRID : Half Million Hands👋
👉Russian Sberbank opens HaGRID, enormous dataset for HGR. "Peace" label is present 🔵🟡
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅552,992 samples, 18 classes
✅HD resolution in RGB format
✅BBox, gesture, leading hands
✅Dataset/models available
More: https://bit.ly/3n2cd8r
🦕 SAVi++: Segmentation by #Google 🦕
👉Novel unsupervised object-centric #AI to predict depth signals from slot-based video representation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Segmenting complex dynamic scenes
✅Static/Moving objects on naturalistic BG
✅LiDAR-SAVi: segmenting in the wild
✅Source code and model soon available!
More: https://bit.ly/3n3hywd
🧠 Bias in #AI, explained simple 🧠
👉Asking DallE-Mini to help me to show what the BIAS in #AI is
𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐝 𝐒𝐚𝐦𝐩𝐥𝐞𝐬:
✅Best eng.->men/Caucasians
✅Best doctors->men/Caucasians
✅Top CEOs->men/Caucasians
✅Chef, kitchen->men/Caucasians
✅Rich People->only Caucasians
✅Poor People->non-Caucasians
✅Italian engineers->back in 30's
✅Chinese eng.->infrastructures
✅Italian working->local market
✅Chinese working->vegetables
✅Men workers->constructions
✅Women workers->only office
More: https://bit.ly/3b0UFqd
🏄🏻♀️Neural Super-Resolution in Movies🏄🏻♀️
👉Implicit neural representation to get arbitrary spatial resolution & FPS -> Super Resolution!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video as continuous video representation
✅Clips in arbitrary space/time resolution
✅OOD generalization in space-time
✅Source code and models available
More: https://bit.ly/3xsqccf
🐹 NOAH just open-sourced! 🐹
👉A novel approach to find the optimal design of prompt modules through NAS algos.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NOAH from Neural prOmpt seArcH
✅Parameter-efficient “prompt modules”
✅Efficient NAS-based implementation
✅Better than transfer, few-shot & domain gen.
More: https://bit.ly/3MKfVhi
👗BodyMap: Hyper-Detailed Humans👗
👉#META unveils 1st-ever dense continuous correspondence for clothed humans
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅1st-ever dense continuous corresp.
✅HQ fingers, hair, and clothes
✅Novel ViT-based architecture
✅SOTA on DensePose COCO
More: https://bit.ly/39nEPps
🧨 Scaling Transformers to GigaPixels!🧨
👉Novel ViT called Hierarchical Image Pyramid Transformer (HIPT) -> Scaling to GigaPixels!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Gigapixel whole-slide imaging (WSI)
✅Leveraging natural hier. structure of WSI
✅Self-supervised Hi-Res representations
✅Source code and models available!
More: https://bit.ly/3xLuzkg
🔥One Millisecond Backbone. Fire!🔥
👉MobileOne by #Apple: efficient mobile backbone with inference <1 ms on #iPhone12!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅75.9% top-1 accuracy on ImageNet
✅38× faster than MobileFormer net
✅Classification, detection & segmentation
✅Source code & model soon available!
More: https://bit.ly/3tsT7f2
🎒 EG3D: source code is out! 🎒
👉#Nvidia just opened EG3D: real time multi-view faces w/ HQ #3D geometry!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Tri-plane-based 3D GAN framework
✅Pose-correlated attribute (expression)
✅SOTA in uncond. 3D-aware synthesis
✅Source code & models NOW available!
More: https://bit.ly/3aOfHs0
🏖️ HumanNeRF: source code is out! 🏖️
👉Pausing the video at any frame and rendering the subject from arbitrary views!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Synthesizing photorealistic humans
✅Synthesizing details, ie. cloth & face
✅Volumetric canonical T-pose
✅Skeletal rigid/non-rigid decomposition
More: https://bit.ly/3NEkTNY
⚽ Zero to #Messi with #deeplearning ⚽
👉EA unveils a neural system to learn multiple soccer juggling skills 😍
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Learning difficult soccer juggling skills
✅Layer-wise mixture-of-experts architecture
✅Specialization arises naturally
✅Adaptive random walk training strategy
More: https://bit.ly/3mwRaL2
🏇🏻Neural Clips by #Nvidia: INSANE 🏇🏻
👉Neural generation with changes in camera viewpoint & content that arises over time 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel hierarchical generator architecture
✅Temp. receptive field + temporal embed.
✅Multi-res. with super-resolution network
✅SOTA in long clip with motion & changes
✅Code, data & models in August 2022 🏖️
More: https://bit.ly/3zroWsC
🍏 Open Source Vision from #Apple 🍏
👉CVNets: open-source (not a joke) lib for neural vision.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅PyTorch-based neural lib. for vision
✅Train 2−4× longer w/ augmentations
✅Plug-and-play components for CV
✅Source code under a custom license
More: https://bit.ly/39d1dSj
🔴 Geogram: geometric algos in C++ 🔴
👉Novel open-source programming library with (research) geometric algorithms in C++
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Geometry Processing from #INRIA
✅30+ papers from SIGGRAPH, etc.
✅Grants: GOODSHAPE & VORPALINE
✅Code (mostly C++) under BSD 3
More: https://bit.ly/3mhS4L7
🧘🏻♂️YogNet: neural yoga assistant🧘🏻♂️
👉Multi-person yoga neural expert for 20 asanas
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅CNNs & reg.LSTMs + 3D-CNNs
✅Multi-person asanas in real-time
✅YAR: dataset for yoga & posture
✅1206 videos, 2D RGB camera
More: https://bit.ly/3NncVbE
🐢 Transformer-Based Sens-Fusion 🐢
👉Updating TransFuser (CVPR21): image + LiDAR representations with self-attention
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Existing approach can't handle traffic 😢
✅Novel multi-modal fusion transformer
✅The new SOTA in driving performance
✅Reducing avg collisions per KM by 48%
✅Insights on current limitations of E2E
More: https://bit.ly/391dmd6
