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

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Малайзия2 240 Технологии и приложения7 726...

📈 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 17 151 подписчиков, занимая 7 726 место в категории Технологии и приложения и 2 240 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 151 подписчиков.

Согласно последним данным от 21 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -166, а за последние 24 часа — -6, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 23.63%. В первые 24 часа после публикации контент обычно набирает 6.86% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 4 057 просмотров. В течение первых суток публикация набирает 1 177 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 22 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.

17 151

Подписчики

-624 часа

-277 дней

-16630 день

4 057

Просмотры поста

~ 1 17724 часа

~ 1 35348 часов

23.63%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

beta

Архив постов

17 151

💦 ObjectDrop: automagical objects removal 💦 👉#Google unveils ObjectDrop, the new SOTA in photorealistic object removal and insertion. Focus on shadows and reflections, impressive! 👉Review https://t.ly/ZJ6NN 👉Paper https://arxiv.org/pdf/2403.18818.pdf 👉Project https://objectdrop.github.io/

17 151

🏀 MAVOS Object Segmentation 🏀 👉MAVOS is a transformer-based VOS that introduces a novel, optimized and dynamic long-term modulated cross-attention memory. Code & Models announced (coming soon under BSD 3-Clause)💙 👉Review https://t.ly/SKaRG 👉Paper https://lnkd.in/dQyifKa3 👉Project github.com/Amshaker/MAVOS 👉Code/Demo (announced)

17 151

☔ AiOS: All-in-One-Stage Humans ☔ 👉All-in-one-stage framework for SOTA multiple expressive pose and shape recovery without additional human detection step. 👉Review https://t.ly/ekNd4 👉Paper https://arxiv.org/pdf/2403.17934.pdf 👉Project https://ttxskk.github.io/AiOS/ 👉Code/Demo (announced)

17 151

💄TinyBeauty: 460 FPS Diffusion Make-up💄 👉TinyBeauty: only 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile! 👉Review https://t.ly/LG5ok 👉Paper https://arxiv.org/pdf/2403.15033.pdf 👉Project https://tinybeauty.github.io/TinyBeauty/

17 151

💄💄TinyBeauty: 460 FPS Diffusion Make-up💄💄 👉TinyBeauty;:necessitates merely 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile! Authors: Jiao Tong University, Alibaba, USC-SJTU. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅DAL, Data Amplify Learning: novel learning framework ✅Diffusion-based Data Amplifier for better training ✅Only 80K parameters to achieve the previous SOTA ✅Insane inference speed (460 fps) on iPhone 13 ✅Highly competitive using only FIVE image pairs #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2403.15033.pdf 👉Project https://tinybeauty.github.io/TinyBeauty/

17 151

🦖 T-Rex 2: a new SOTA is out! 🦖 👉A novel (VERY STRONG) open-set object detector model. Strong zero-shot capabilities, suitable for various scenarios with only one suit of weights. Demo and Source Code released💙 👉Review https://t.ly/fYw8D 👉Paper https://lnkd.in/dpmRh2zh 👉Project https://lnkd.in/dnR_jPcR 👉Code https://lnkd.in/dnZnGRUn 👉Demo https://lnkd.in/drDUEDYh

17 151

🦕 DINO-based Video Tracking 🦕 👉The Weizmann Institute announced the new SOTA in point-tracking via pre-trained DINO features. Source code announced (not yet released)💙 👉Review https://t.ly/_GIMT 👉Paper https://lnkd.in/dsGVDcar 👉Project dino-tracker.github.io/ 👉Code (announced)

17 151

🪼FaceXFormer: Unified Face-Transformer🪼 👉FaceXFormer, the first unified transformer for facial analysis: face parsing, landmark detection, head pose, attributes recognition, age, gender, race, and landmarks. 👉Review https://t.ly/MfAFI 👉Paper https://arxiv.org/pdf/2403.12960.pdf 👉Project kartik-3004.github.io/facexformer_web/ 👉Code github.com/Kartik-3004/facexformer

17 151

🏷️ Face Foundation Model 🏷️ 👉Arc2Face, the first foundation model for human faces. Large dataset of high-resolution faces with consistent ID / intra-class variability, and an ID-conditioned face model trained on it. Source Code released 💙 👉Review https://t.ly/MfAFI 👉Paper https://lnkd.in/dViE_tCd 👉Project https://lnkd.in/d4MHdEZK 👉Code https://lnkd.in/dv9ZtDfA

17 151

🏷️🏷️Arc2Face: Face Foundation Model🏷️🏷️ 👉Arc2Face, the first foundation model for human faces. Large dataset of high-resolution faces with consistent ID / intra-class variability, and an ID-conditioned face model trained on it. Source Code released 💙 #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://lnkd.in/dViE_tCd 👉Project https://lnkd.in/d4MHdEZK 👉Code https://lnkd.in/dv9ZtDfA

17 151

🪖RT Humanoid from Head-Mounted Sensors🪖 👉#META (+CMU) announced SimXR, a method for controlling a simulated avatar from info obtained from AR/VR headsets 👉Review https://t.ly/Si2Mp 👉Paper arxiv.org/pdf/2403.06862.pdf 👉Project www.zhengyiluo.com/SimXR/

17 151

👺 Can GPT-4 play DOOM? 👺 👉Apparently yes, GPT-4 can play the game to a passable degree: it is able to manipulate doors, combat enemies, and perform pathing. Code (with licensing restrictions) released 👉Review https://t.ly/W8-0F 👉Paper https://lnkd.in/dmsB7bjA 👉Project https://lnkd.in/ddDPwjQB

17 151

🏛️ PIXART-Σ: 4K Generation 🏛️ 👉PixArt-Σ is a novel Diffusion Transformer model (DiT) capable of directly generating images at 4K resolution. Authors: #Huawei, Dalian, HKU & HKUST. Demos available, code announced 💙 👉Review https://t.ly/Cm2Qh 👉Paper arxiv.org/pdf/2403.04692.pdf 👉Project pixart-alpha.github.io/PixArt-sigma-project/ 👉Repo (empty) github.com/PixArt-alpha/PixArt-sigma 🤗-Demo https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha

17 151

🦁StableDrag: Point-based Editing🦁 👉#Tencent unveils StableDrag, a novel point-based image editing framework via discriminative point tracking method + confidence-based latent enhancement strategy for motion supervision. Source Code announced but still no repo. 👉Review https://t.ly/eUI05 👉Paper https://lnkd.in/dz8-ymck 👉Project stabledrag.github.io/

17 151

🧵E-LoFTR: new Feats-Matching SOTA🧵 👉A novel LoFTR-inspired algorithm for efficiently producing semidense matches across images: up to 2.5× faster than LoFTR, superior to previous SOTA pipeline (SuperPoint + LightGlue). Code announced. 👉Review https://t.ly/7SPmC 👉Paper https://arxiv.org/pdf/2403.04765.pdf 👉Project https://zju3dv.github.io/efficientloftr/ 👉Repo https://github.com/zju3dv/efficientloftr

17 151

🔥 SOTA: Stable Diffusion 3 is out! 🔥 👉Stable Diffusion 3 is the new SOTA in text-to-image generation (based on human preference evaluations). New Multimodal Diffusion Transformer (MMDiT) architecture uses separate sets of weights for image & language, improving text understanding/spelling capabilities. Weights & Source Code released 💙 👉Review https://t.ly/a1koo 👉Paper https://lnkd.in/d4i-9Bte 👉Blog https://lnkd.in/d-bEX-ww

17 151

💥 MM-AU: Accident Understanding 💥 👉MM-AU - Multi-Modal Accident Video Understanding: 11,727 videos with temporally aligned text descriptions. 2.23M+ BBs and 58,650 pairs of video-based accident reasons. Dataset & Code released 💙 👉Review https://t.ly/a-jKI 👉Paper https://arxiv.org/pdf/2403.00436.pdf 👉Dataset http://www.lotvsmmau.net/MMAU/demo

17 151

💌 Multi-LoRA Composition 💌 👉Two novel training-free image composition: LoRA Switch and LoRA Composite for integrating any number of elements in an image through multi-LoRA composition. Source Code released 💙 👉Review https://t.ly/GFy3Z 👉Paper arxiv.org/pdf/2402.16843.pdf 👉Code github.com/maszhongming/Multi-LoRA-Composition

17 151

🎷EMO: talking/singing Gen-AI 🎷 👉#Alibaba announced EMO: audio-driven portrait-video generation. Vocal avatar videos with expressive facial expressions, and various head poses. Input: 1 single frame, video duration according to the length of input audio 👉Review https://t.ly/4IYj5 👉Paper https://lnkd.in/dGPX2-Yc 👉Project https://lnkd.in/dyf6p_N3 👉Repo (empty) github.com/HumanAIGC/EMO

17 151

🎷EMO: talking/singing Gen-AI 🎷 👉#Alibaba announced EMO: audio-driven portrait-video generation. Vocal avatar videos with expressive facial expressions, and various head poses. Input: 1 single frame, video duration according to the length of input audio 👉Review 👉Paper https://lnkd.in/dGPX2-Yc 👉Project https://lnkd.in/dyf6p_N3 👉Repo (empty) github.com/HumanAIGC/EMO