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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 17 144 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 17 144 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.
17 144
Suscriptores
+324 horas
-367 días
-18630 días
Archivo de publicaciones
🚜NeDDF: the NeRF evolution!🚜
👉Novel 3D representation that reciprocally constrains distance & density fields
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF provides no distance
✅Extending for arbitrary density
✅Density via dist-field & gradient
✅Alleviating the instability
More: https://bit.ly/3Bte8LC
🍏🍏 GAUDI: the Neural Architect 🍏🍏
👉Novel generative model for immersive 3D scenes from a moving camera
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hundreds of thousands pics/scenes
✅Novel denoising optimization objective
✅New SOTA across multiple datasets
✅Un/conditional on images/text
More: https://bit.ly/3Bt65ye
🍦🍦 Rewriting Geometry of GAN 🍦🍦
👉Drive GAN synthesizing many unseen objects with the desired shape
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅User-friendly "warping" with geometry
✅Low-rank update to layer for editing
✅Latent augmentation based on style-mix
✅Endless objects with defined changes
✅Latent space interpolation, image editing
More: https://bit.ly/3zIfOj8
🏙️ CityNeRF: Neural Rendering of City Scenes 🏙️
👉Progressive NeRF model and training set on city-scenes
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅BungeeNeRF: novel progressive NeRF
✅Details on drastically varied scales
✅Growing with residual block structure
✅Inclusive multi-level data supervision
More: https://bit.ly/3cS9vk7
🎩ShAPO: SOTA in object understanding🎩
👉Joint multi-object detection, #3D texture, 6D object pose & size estimation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Disentangled shape & appearance
✅Efficient octree-based differentiable
✅Object-centric understanding pipeline
✅Detection, reconstruction , 6D & size
✅SOTA in reconstruction & pose est.
More: https://bit.ly/3oHN5EQ
📺 NeRF-ing "The Big Bang Theory" 📺
👉Berkeley unveils an approach for accurate estimation of actor’s 3D pose & location
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Input: images across the whole season
✅3D context (i.e. cams, structure, body)
✅Integrating context in 3D estimation
✅Re-ID, gaze, cinematography, pic editing
✅Knock, Knock, Penny!
More: https://bit.ly/3OLuaUb
🦊 3D-Aware "StyleGANv2" version 🦊
👉Upgrading StyleGANv2 into a novel 3D-aware GAN with just a minimal set of changes🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MPI-like 3D-aware GAN w/ single-view
✅GMPI: generative multiplane image
✅2D GAN 3D-aware with a minimal changes
✅Encoding 3D-aware inductive biases
More: https://bit.ly/3OJ5gnS
🦚 TinyCD: Neural Change Detection 🦚
👉TinyCD: new SOTA in change detection with up to 150x fewer parameters.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SOTA with up to 150X fewer params
✅Mixing blocks for s.t. cross-correlation
✅PW-MLP for pixel wise classification
✅MAMB: novel block for skip connection
More: https://bit.ly/3zFEngk
⚗️ SemAbs: 3D Scene Understanding ⚗️
👉Framework that equips 2D Vision-Language Models (VLMs) with new 3D spatial capabilities
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅2D VLMs with 3D reasoning skills
✅ViTs Efficient MS Relevancy Extraction
✅Novel Open-World understanding tasks
✅Completing partially observed objects
✅Finding hidden objects from language
More: https://bit.ly/3PYYk7d
🎃New SOTA in UDA Semantic Seg.🎃
👉HRDA: multi-res Unsupervised Domain Adaptive Semantic Seg. -> SOTA
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ETH + MPG + KU Leuven 🤯
✅HRDA: multi-res approach for UDA
✅Manageable GPU memory footprint
✅Small objects & fine segmentation detail
✅New SOTA on GTA and Synthia dataset
More: https://bit.ly/3cKtDEp
🔥🔥 UPDATE 🔥🔥
Code Released: https://github.com/apple/ml-mobileone
🧱 Assembling #LEGO with #AI 🧱
👉Step-by-step assembly manual created by human into machine-interpretable instructions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Stanford + MIT + #Google 🤯
✅MEPNet: Manual-to-Executable-Plan Net
✅Manual to machine-executable plan
✅2D manual - 3D geometric shape
✅Reasoning on 3D alignments of legos
More: https://bit.ly/3PCwn5C
💄DEVIANT: SOTA in mono-3D detection💄
👉A novel Depth EquiVarIAnt NeTwork for 3D monocular detection in the wild
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Michigan + #Meta + Ford 🤯
✅Depth-equi. + scale equiv. steerable
✅New SOTA on KITTI & Waymo
✅Ok cross-dataset -> generalization
More: https://bit.ly/3OEFtgK
👹Multiface Neural Rendering 👹
👉A new multi-view, Hi-Res data collected at #META Reality Labs for neural face
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Mugsy, large scale multi-cam apparatus
✅High-Res sync facial performance
✅Closing the gap in accessing HQ data
✅Suitable for #VR & #mixedreality
More: https://bit.ly/3b6XfeL
🎷🎷OMNI3D: #3D Objects in the Wild🎷🎷
👉#3D detection: 234k images, 3M+ instances & 97 categories
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅OMNI3D from publicly released dataset
✅234k pics, 3M+ annotation with 3D box
✅97 categories such as sofa, table, cars
✅Fast (450x) and exact algorithm for IoU
✅Cube R-CNN: novel 3D object detector
More: https://bit.ly/3cznjzG
🔥 #AIwithPapers: we are 3,500+! 🔥
💙💛 Ready for YOLO 10, 11, π, ∞, Ψ, and more? The more we are, the faster we catch'em all 💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
🪰NUWA-Infinity is out!🪰
👉∞ generation by #Microsoft: arbitrarily-sized HD images and long videos 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unconditional Image Gen.
✅Text-to-Image/Text-to-Clip
✅Animation / Out-painting
✅Hi-res, arbitrary long clip
✅NCP for patches caching
More: https://bit.ly/3zmBf9f
🦚 TimeLens++: Event-based Interpolation 🦚
👉Novel event-based interpolation with non-linear flow & multi-scale fusion
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel motion spline estimator
✅Non-linear continuous event/frames flow
✅Multi-feature fusion, gated compression
✅Novel hybrid dataset with 100+ videos
More: https://bit.ly/3yJyY6g
🔥Important update from #OpenAI🔥
👉 https://openai.com/blog/reducing-bias-and-improving-safety-in-dall-e-2/
💣 HD Neural Avatar @130FPS 💣
👉Samsung unveils MegaPortraits: novel one-shot creation of HD neural human avatar
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅One-shot neural avatars, SOTA up 512p
✅"Upgrading" to megapixel via more pics
✅First Neural Head Avatars in HD
✅Up to to 130 FPS via #GPU
More: https://bit.ly/3oboWWT
