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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 17 168 подписчиков, занимая 7 718 место в категории Технологии и приложения и 2 234 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 168 подписчиков.
Согласно последним данным от 20 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -169, а за последние 24 часа — 0, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 22.86%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 926 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 26.
- 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 21 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.
17 168
Подписчики
Нет данных24 часа
-357 дней
-16930 день
Архив постов
🤬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
👽DAM for SAM2 Tracking👽
👉From the University of Ljubljana a novel distractor-aware drop-in memory module for SAM2. Reducing the tracking drift toward distractors and improves redetection capability after object occlusions. DAM4SAM outperforms SAM2.1, SOTA on 10 benchmarks. Repo released 💙
👉Review https://t.ly/8aR59
👉Paper https://arxiv.org/pdf/2509.13864
👉Project jovanavidenovic.github.io/dam-4-sam/
👉Repo github.com/jovanavidenovic/DAM4SAM
I’m keeping the channel free from users’ interactions to avoid SPAM. The only way to interact is commenting in the subchannels after being accepted. Do you like this setting?
I’m keeping the channel free from interaction to avoid SPAM. The only way to interact is commenting the post after being accepted in the subchannel. Do you like this setting?
🍕 Superpixel Anything (SOTA) 🍕
👉 SuperPixel Anything Model, a versatile framework for segmenting images. Extracting image features for superpixel generation blended with a large-scale pretrained model for semantic-agnostic segmentation to ensure superpixels alignement with masks. Damn romantic. Repo & Dataset available💙
👉Review https://t.ly/rpxRh
👉Paper arxiv.org/pdf/2509.12791
👉Repo github.com/waldo-j/spam
🛡️3D Prompted Vision-LLM🛡️
👉#Nvidia unveils SR-3D a novel aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. Flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. Code & Dataset announced💙
👉Review https://t.ly/5Y2c5
👉Paper https://arxiv.org/pdf/2509.13317
👉Project https://www.anjiecheng.me/sr3d
👉Repo TBA
🔥🔥 How We Use ChatGPT 🔥🔥
👉By July 2025, ChatGPT has 700M+ users sending more than 2.5B+ messages per day. About 29,000 messages per second. This paper documents eight important facts about ChatGPT usage in the last three years. 63 pages of impressive statistics. To read.💙
👉Review https://t.ly/QYHSi
🦠🦠 Segment & Track Any Cell 🦠
👉RWTH unveils a novel zero-shot cell tracking framework by integrating Segment Anything 2 (SAM2) into the tracking pipeline. Source Code released💙
👉Review https://t.ly/n_srg
👉Paper https://arxiv.org/pdf/2509.09943
👉Repo https://github.com/zhuchen96/sam4celltracking
