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
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 17 264 подписчиков, занимая 7 532 место в категории Технологии и приложения и 2 128 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 264 подписчиков.
Согласно последним данным от 30 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 223, а за последние 24 часа — 3, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 16.65%. В первые 24 часа после публикации контент обычно набирает 7.01% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 874 просмотров. В течение первых суток публикация набирает 1 211 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 16.
- 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 31 октября, 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.
17 264
Подписчики
+324 часа
+37 дней
+22330 дней
Архив постов
🔥🔥 Neural Segmentation on fire 🔥🔥
👉Novel methods for segmentation with mask calibration. Robustness++ in VOS.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Study: VOS robustness vs. perturbations
✅Adaptive object proxy (AOP) aggregation
✅Less errors due unstable pixel-level match
✅Code/models (should be) available soon
More: https://bit.ly/3yhIY6Q
🔥🔥 HD Dichotomous Segmentation 🔥🔥
👉 A new task to segment highly accurate objects from natural images.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅5,000+ HD images + accurate binary mask
✅IS-Net baseline in high-dim feature spaces
✅HCE: model vs. human interventions
✅Source code (should be) available soon
More: https://bit.ly/3ah2BDO
🔥YOLOv7: YOLO for segmentation🔥
👉YOLOv7: adding a lot of newer skills to the YOLO architecture family.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅YOLOv7, not a successor of YOLO family!
✅Framework for detection & segmentation
✅Applications based on #META detectron2
✅DETR & ViT detection out-of-box
✅Easy support for pipeline thought #ONNX
✅YOLOv4 + InstanceSegm. via single stage
✅The latest YOLOv6 training is supported!
✅Source code under GPL license.
More: https://bit.ly/3ysSJAp
🐪 BlazePose: Real-Time Human Tracking 🐪
👉Novel real-time #3D human landmarks from #google. Suitable for mobile.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MoCap from single RGB on mobile
✅Avatar, Fitness, #Yoga & AR/VR
✅Full body pose from monocular
✅Novel 3D ground truth acquisition
✅Additional hand landmarks
✅Fully integrated in #MediaPipe
More: https://bit.ly/3uvyiAv
🔥🔥YOLOv6 is out: PURE FIRE!🔥🔥
👉YOLOv6 is a single-stage object detection framework for industrial applications
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Efficient Decoupled Head with SIoU Loss
✅Hardware-friendly for Backbone/Neck
✅520+ FPS on T4 + TensorRT FP16
✅Released under GNU General Public v3.0
More: https://bit.ly/3OLjncK
🥶 E2V-SDE: biggest troll ever? 🥶
👉E2V-SDE paper (accepted to #CVPR2022) consists of texts copied from 10+ previously published papers 😂
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent ODEs for Irregularly-Sampled TS
✅Stochastic Adversarial Video Prediction
✅Continuous Latent Process Flows
✅More papers....
More: https://bit.ly/3bsL8Zw (AUDIO ON!)
🗺️Neural Translation Image -> Map🗺️
👉A novel method for instantaneous mapping as a translation problem
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Bird’s-eye-view (BEV) map from image
✅A restricted data-efficient transformer
✅Monotonic attention from lang.domain
✅SOTA across several datasets
More: https://bit.ly/39MQ76Z
🫀I M AVATAR: source code is out!🫀
👉Neural implicit head avatars from monocular videos
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅#3D morphing-based implicit avatar
✅Detailed Geometry/appearance
✅D-Rendering e2e learning from clips
✅Novel synthetic dataset for evaluation
More: https://bit.ly/3A2yzy9
🍔 Fully Controllable "NeRF" Faces 🍔
👉Neural control of pose/expressions from single portrait video
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF-control of the human head
✅Loss of rigidity by dynamic NeRF
✅3D full control/modelling of faces
✅No source code or models yet 😢
More: https://bit.ly/3OEjwi7
🦋Transf-Codebook HD-Face Restoration🦋
👉S-Lab unveils CodeFormer: hyper-datailed face restoration from degraded clips
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Face restoration as a code prediction
✅Discrete CB prior in small proxy space
✅Controllable transformation for LQ->HQ
✅Robustness and global coherence
✅Code and models soon available
More: https://bit.ly/3QEa9B5
🦑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
