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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
Больше📈 Аналитический обзор Telegram-канала AI with Papers - Artificial Intelligence & Deep Learning
Канал AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 17 136 подписчиков, занимая 7 701 место в категории Технологии и приложения и 2 225 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 136 подписчиков.
Согласно последним данным от 25 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -186, а за последние 24 часа — 3, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 23.94%. В первые 24 часа после публикации контент обычно набирает 6.86% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 0 просмотров. В течение первых суток публикация набирает 1 177 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как framework, object, dataset, tba, depth.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 26 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
17 136
Подписчики
+324 часа
-367 дней
-18630 день
Архив постов
🚜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
