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

Malasia2 240 Tecnologías y Aplicaciones7 726...

📈 Análisis del canal de Telegram AI with Papers - Artificial Intelligence & Deep Learning

El canal AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 17 151 suscriptores, ocupando la posición 7 726 en la categoría Tecnologías y Aplicaciones y el puesto 2 240 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 151 suscriptores.

Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -166, y en las últimas 24 horas de -6, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 23.63%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 6.86% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 4 057 visualizaciones. En el primer día suele acumular 1 177 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 26.
Intereses temáticos: El contenido se centra en temas clave como framework, object, dataset, tba, depth.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 22 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

17 151

Suscriptores

-624 horas

-277 días

-16630 días

4 057

Visitas de la publicación

~ 1 17724 horas

~ 1 35348 horas

23.63%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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