AI with Papers - Artificial Intelligence & Deep Learning
Открыть в Telegram
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
👉 A proof I'm not a bot...
My (short) interview to one of the biggest Italian media: AI in 2016, HPC / Quantum and how I created my startup: https://www.linkedin.com/posts/visionarynet_ai-itw25-ai-activity-7381215486115643392-t7an
Thanks for the support (and of course a new paper coming in a few hours)
🎷🎷 Clink! Chop! Thud! 🎷🎷
👉Sounding Object Detection: while an environment may contain many objects, only a few are directly involved in producing sound during an interaction. This model detects the sounding object given a video of an object interaction. Code/Data announced💙
👉Review https://t.ly/VK_1h
👉Paper https://lnkd.in/depNjVXm
👉Project https://lnkd.in/dF63EZFG
👉Repo TBA
🔩Code-Centric Agentic Education🔩
👉Show Lab unveils Code2Video: agentic, code-centric framework that generates HQ educational videos from knowledge points. Unlike pixel-based text-to-video models, this approach leverages executable Manim code to ensure clarity, coherence & reproducibility. Repo under MIT💙
👉Review https://t.ly/Fv4LJ
👉Paper https://arxiv.org/pdf/2510.01174
👉Repo https://github.com/showlab/Code2Video/
👉Project https://showlab.github.io/Code2Video/
👩🦱Physical-Hair Diffusion👩🦱
👉CONTROLHAIR is novel hybrid framework that integrates a physics simulator with conditional video diffusion to enable controllable dynamic hair rendering. Repo announced💙
👉Review https://t.ly/78LHr
👉Paper https://lnkd.in/epm-A9Fq
👉Project https://lnkd.in/evsjz298
👉Repo TBA
👔 Universal Image Restoration 👔
👉LucidFlux by HKUSTGZ is the universal image restoration framework built on a large-scale diffusion transformer that delivers photorealistic restorations of real-world low-quality (LQ) images, outperforming SOTA diffusion-based models across diverse degradations. Repo under custom Non-Commercial License💙
👉Review https://t.ly/Z5cA3
👉Paper https://arxiv.org/pdf/2509.22414
👉Project https://w2genai-lab.github.io/LucidFlux/
👉Repo https://github.com/W2GenAI-Lab/LucidFlux
🤖 Real-time Interactive Long Video 🤖
👉LONGLIVE by #Nvidia is a frame-level autoregressive framework for real-time & interactive long video generation. LONGLIVE accepts sequential user prompts and generates corresponding videos in real time. Repo under non-commercial license💙
👉Review https://t.ly/jJkdY
👉Paper arxiv.org/pdf/2509.22622
👉Project nvlabs.github.io/LongLive/
👉Repo github.com/NVlabs/LongLive
🤗huggingface.co/Efficient-Large-Model/LongLive-1.3B
🔥SOTA Detection w/ DINOv3🔥
👉DEIMv2 is the evolution of DEIM framework while leveraging DINOv3. Various model sizes, from an ultra-light version up to S, M, L, & X for a wide range of scenarios. Across these variants, DEIMv2 achieves SOTA. Repo Apache2.0💙
👉Review https://t.ly/P7jEH
👉Paper arxiv.org/pdf/2509.20787
👉Repo github.com/Intellindust-AI-Lab/DEIMv2
👉Project intellindust-ai-lab.github.io/projects/DEIMv2
🤬Music vs. Face Recognition🤬
👉At their recent concerts, Massive Attack turned surveillance into art: crafting a haunting audiovisual show that exposes the dangers of facial recognition tech. Audiences were scanned with mock surveillance, confronting them with the intrusive power of #AI in real time
👉Review https://t.ly/VMrPC
👉News https://t.ly/aj1an
A few “leaks” for you from the #Nvidia presentation I’m right now. Impressive stuff.
Ps: sorry for the shitty pics ♥️
🌀 CLOPS: Vision-Driven Avatar 🌀
👉CLOPS is the first human avatar solely uses egocentric vision to perceive its surroundings and navigate. CLOPS is able to realistically move in a scene and use egocentric vision in order to find a goal by combining a data driven low level motion prior with a Q-Learning policy. A loop of visual perception & motion. Code announced💙
👉Review https://t.ly/RXp64
👉Paper https://arxiv.org/pdf/2509.19259
👉Project markos-diomataris.github.io/projects/clops/
👉Repo TBA
🏆 MOSEv2 Challenge 2025 Winner 🏆
👉A practical solution for complex segmentation based on the Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. Repo under Apache 2.0💙
👉Review https://t.ly/2MjNm
👉Paper https://arxiv.org/pdf/2509.19183
👉Paper (SeC) https://arxiv.org/pdf/2507.15852
👉Project https://rookiexiong7.github.io/projects/SeC/
👉Repo https://github.com/OpenIXCLab/SeC
🫓 WINNER of LSVOS Challenge 🫓
👉SaSaSa2VA introduces Segmentation Augmentation to improve global video understanding while remaining efficient, and employs Selective Averaging at inference to robustly fuse complementary predictions. This approach achieves SOTA on the 7th LSVOS Challenge (RVOS track). A practical solution with full repo under Apache💙
👉Review https://t.ly/aH4mB
👉Paper https://arxiv.org/pdf/2509.16972
👉Repo https://github.com/magic-research/Sa2VA
🐳 Invariant Saliency Detection 🐳
👉SI-SOD: invariant salient object detection is a paper investigating a fundamental yet underexplored issue in Salient Object Detection (SOD): the size-invariant property for evaluation protocols, particularly in scenarios when multiple salient objects of significantly different sizes appear within a single image. Repo released💙
👉Review https://lnkd.in/p/dZBfbSsf
👉Paper https://arxiv.org/pdf/2509.15573
👉Project https://ferry-li.github.io/SI_SOD/
👉Repo https://github.com/Ferry-Li/SI-SOD
🔥🔥 It's time to decide whether you want to give LinkedIn your data for AI training or not 🔥🔥
Set here: http://linkedin.com/mypreferences/d/settings/data-for-ai-improvement
