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 17 262 подписчиков, занимая 7 491 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 17 262 подписчиков.
Согласно последним данным от 23 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 255, а за последние 24 часа — 2, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 18.93%. В первые 24 часа после публикации контент обычно набирает 7.17% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 267 просмотров. В течение первых суток публикация набирает 1 237 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 20.
- 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 24 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.
17 262
Подписчики
+224 часа
+67 дней
+25530 дней
Архив постов
🔪Tracking Object Transformations🔪
👉"Track Any State": tracking objects through transformations while detecting/describing state changes. Repo & Dataset available under MIT💙
👉Review https://t.ly/NPyW4
👉Paper https://lnkd.in/d4pA3bXJ
👉Project https://lnkd.in/dgbNfCuj
👉Repo https://lnkd.in/dtVWq2z7
Greetings from the SMART CITY WORLD CONGRESS in Barcellona. If you are around, ping me ;)
👢Generative View Stitching 👢
👉GVS is a novel approach that enables collision-free camera-guided video generation for predefined trajectories, it's a non-autoregressive alternative to video length extrapolation. Full repo under MIT💙
👉Review https://t.ly/TiN_5
👉Paper https://arxiv.org/pdf/2510.24718
👉Project https://andrewsonga.github.io/gvs/
👉Repo github.com/andrewsonga/generative_view_stitching
🌱PlanarTrack: Large Planar Tracking🌱
👉PlanarTrack is a large-scale HQ and challenging benchmark for planar tracking: 1,150 sequences with 733K+ frames, including 1,000 short-term & 150 long-term videos. Repo & Dataset available💙
👉Review https://t.ly/mYNi7
👉Paper arxiv.org/pdf/2510.23368
👉Repo https://lnkd.in/edb3GMyT
👉Project https://lnkd.in/eC-hVB-U
👉Data https://lnkd.in/eew2j4tM
🦄Unified Region-Level MLLM🦄
👉PixeRefers is an unified multimodal LLM framework that supports precise, region-specific understanding in both static images and dynamic videos, overcoming the holistic, scene-level bias of prior MLLMs. SOTA results. Demo, Repo & Dataset available💙
👉Review https://t.ly/WH4dQ
👉Paper arxiv.org/pdf/2510.23603
👉Project circleradon.github.io/PixelRefer
👉Repo https://github.com/alibaba-damo-academy/PixelRefer
🧷Generative Point Tracking w/ FM🧷
👉Generative Point Tracker (GenPT) is a novel generative framework for modelling multi-modal trajectories. Able to capture the multi-modality in point trajectories. Repo under MIT💙
👉Review https://t.ly/MMFrt
👉Paper https://arxiv.org/pdf/2510.20951
👉Project mtesfaldet.net/genpt_projpage/
👉Repo https://github.com/tesfaldet/genpt
🦗Character Mixing Generation🦗
👉MBZUAI unveils the first ever video-gen system able to preserve character ID, behavior & original style while generating plausible interactions between characters that have never coexisted - from cartoons (We Bare Bears, Tom & Jerry) to realistic humans (Mr. Bean, Young Sheldon)
👉Review https://t.ly/tN84a
👉Paper https://lnkd.in/dhKMwukv
👉Project https://lnkd.in/dBkJs48h
👉Repo https://lnkd.in/dw_uzgAk
🐠ITTO: Protocol for Dynamic Tracking🐠
👉ITTO by Caltech is a novel long-range tracking benchmark suite for evaluating and diagnosing tracking methods on complex and long-range motions. Repo under CC BY-NC 4.0💙
👉Review https://t.ly/tN84a
👉Paper https://arxiv.org/pdf/2510.19819
👉Project https://glab-caltech.github.io/ITTO/
👉Repo https://github.com/ilonadem/itto
🏜️Omni Driving Navigation Models🏜️
👉OmniNWM is a unified panoramic navigation world model that advances autonomous driving by jointly generating multi-modal states (RGB, semantics, depth, 3D occupancy), enabling precise action control & facilitating closed-loop evaluation through occupancy-based dense rewards. Repo under Apache 2.0💙
👉Review https://t.ly/ktXvz
👉Paper https://lnkd.in/eFKSZnrc
👉Project https://lnkd.in/eSDfccv8
👉Repo https://lnkd.in/efCSvjtp
Repo (pretty empty) now online: https://github.com/OatmealLiu/UrbanVerse
🔥 SAM 2++: Track Anything 🔥
👉SAM 2++ is a novel unified model towards tracking at any granularity, including masks, boxes, and points. Impressive results but no code announced yet 😢
👉Review https://t.ly/I392_
👉Paper arxiv.org/pdf/2510.18822
👉Project tracking-any-granularity.github.io/
👉Repo :(
+2
🌵All-in-One Dense Keypoints🌵
👉DeepDetect is a novel all-in-one, dense keypoints detector that unifies the strengths of SIFT, ORB, BRISK, FAST, AGAST, Harris, Shi-Tomasi, Canny & Sobel into a neural net. DAMN ROMANTIC. Repo under MIT💙
👉Review https://t.ly/VKGct
👉Paper https://arxiv.org/pdf/2510.17422
👉Repo https://github.com/saktx/DeepDetect
🦄 City-Tour -> Simulation 🦄
👉UrbanVerse is a novel system to convert real-world urban scenes from city-tour videos into physics-aware, interactive simulation environments, enabling scalable robot learning in urban spaces with real-world generalization. Repo & Data announced 💙
👉Review https://t.ly/UvXNS
👉Paper https://arxiv.org/pdf/2510.15018
👉Project https://urbanverseproject.github.io/
👉Repo TBA
🫙Universal Feature Up-Sampling🫙
👉AnyUp is a novel method for feature up-sampling that can be applied to ANY vision feature at ANY resolution, without encoder-specific training: inference-time feature-agnostic up-sampling architecture to improve up-sampling quality. Repo under CC-4.0💙
👉Review https://t.ly/HvEw9
👉Paper https://arxiv.org/pdf/2510.12764
👉Project https://wimmerth.github.io/anyup/
👉Repo https://github.com/wimmerth/anyup
🫧🫧 Detect Anything via MLLM 🫧🫧
👉Rex-Omni is a 3B-multimodal model that unifies visual perception tasks, including object detection, OCR, pointing, key-pointing & visual prompting into a single next point prediction framework. Impressive results. Full repo under IDEA License 1.0💙
👉Review https://t.ly/DCTk_
👉Paper https://lnkd.in/d4VDD-9j
👉Project https://lnkd.in/d6unEyvq
👉Repo https://lnkd.in/dkYJFe-x
↗️ TrackVLA++ Visual Tracking↘️
👉TrackVLA++ is a novel Vision-Language-Action model that incorporates spatial reasoning and target identification memory, enabling SOTA performance in both long-horizon and highly crowded tracking scenarios. Model announced💙
👉Review https://t.ly/ruYzc
👉Paper https://arxiv.org/pdf/2510.07134
👉Project pku-epic.github.io/TrackVLA-plus-plus-Web/
👉Repo TBA
💄Pixel-Perfect Depth (SOTA)💄
👉Pixel-Perfect Depth is a mono-depth estimation model with pixel-space diffusion transformers. New SOTA. Repo under Apache 2.0💙
👉Review https://t.ly/75PGo
👉Paper https://lnkd.in/d8wxFpyY
👉Project https://lnkd.in/dV5HhsqH
👉Repo https://lnkd.in/d9JKFBJq
👉Demo https://lnkd.in/d3wBkKJ9
🎺Visual Grounding RVOS🎺
👉ReferDINO is a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception & cross-modal spatio-temporal reasoning. Code, Demo & checkpoints released💙
👉Review https://t.ly/rOdkP
👉Paper https://lnkd.in/efuAFQdE
👉Project https://lnkd.in/dK3wMZqv
👉Repo https://lnkd.in/d3i2PsNF
🎺Visual Grounding RVOS🎺
👉ReferDINO is a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception & cross-modal spatio-temporal reasoning. Code, Demo & checkpoints released💙
👉Review https://t.ly/rOdkP
👉Paper https://lnkd.in/efuAFQdE
👉Project https://lnkd.in/dK3wMZqv
👉Repo https://lnkd.in/d3i2PsNF
