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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

Malasia2 128 Tecnologías y Aplicaciones7 491...

📈 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 17 261 suscriptores, ocupando la posición 7 491 en la categoría Tecnologías y Aplicaciones y el puesto 2 128 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 261 suscriptores.

Según los últimos datos del 24 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 258, y en las últimas 24 horas de -2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 16.76%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.11% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 893 visualizaciones. En el primer día suele acumular 1 227 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 16.
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 25 octubre, 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.

17 261

Suscriptores

-224 horas

+27 días

+25830 días

2 893

Visitas de la publicación

~ 1 22724 horas

~ 1 55648 horas

16.76%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 259

💥Geo4D: VideoGen 4D Scene💥 👉The Oxford VGG unveils Geo4D: video diffusion for monocular 4D reconstruction. Only synthetic data for training, but strong generalization to real world: point maps, depth & ray maps for the new SOTA in dynamic reconstruction. Code released💙 👉Review https://t.ly/X55Uj 👉Paper arxiv.org/pdf/2504.07961 👉Project geo4d.github.io/ 👉Code github.com/jzr99/Geo4D

17 259

🥊 Pose in Combat Sports 🥊 👉The novel SOTA framework for an accurate physics-based #3D human pose estimation in combat sports w/ sparse multi-cameras setup. Dataset to be released soon💙 👉Review https://t.ly/EfcGL 👉Paper https://lnkd.in/deMMrKcA 👉Project https://lnkd.in/dkMS_UrH

17 259

🧊BoxDreamer Object Pose🧊 👉BoxDreamer is a generalizable RGB-based approach for #3D object pose estimation in the wild, specifically designed to address challenges in sparse-view settings. Code coming, demo released💙 👉Review https://t.ly/e-vX9 👉Paper arxiv.org/pdf/2504.07955 👉Project https://lnkd.in/djz8jqn9 👉Repo https://lnkd.in/dfuEawSA 🤗Demo https://lnkd.in/dVYaWGcS

17 259

💛 Unified Scalable SVG Generator 💛 👉OmniSVG is the first family of e2e multimodal generators that leverages pre-trained VLMs to create detailed SVGs. Code, models & dataset to be released under MIT💙 👉Review https://t.ly/JcR3I 👉Paper https://arxiv.org/pdf/2504.06263 👉Project https://omnisvg.github.io/ 👉Repo github.com/OmniSVG/OmniSVG 👉Dataset https://huggingface.co/OmniSVG

17 259

🐈 TTT Long Video Generation🐈 👉A novel architecture for video generation adapting the CogVideoX 5B model by incorporating Test-Time Training layers. Adding TTT layers into a pre-trained Transformer -> one-minute clip from text storyboards. Videos, code & annotations released💙 👉Review https://t.ly/mhlTN 👉Paper arxiv.org/pdf/2504.05298 👉Project test-time-training.github.io/video-dit/ 👉Repo github.com/test-time-training/ttt-video-dit

17 259

⛽ VoRA: Vision as LoRA ⛽ 👉#ByteDance unveils Vision as LoRA (VoRA), a novel paradigm converting LLMs into Multimodal Large Language Models (MLLMs) by integrating vision-specific LoRA layers. All training data, codes, and model weights available💙 👉Review https://t.ly/guNVN 👉Paper arxiv.org/pdf/2503.20680 👉Repo github.com/Hon-Wong/VoRA 👉Project georgeluimmortal.github.io/vora-homepage.github.io/

17 259

🌳 Compose Anything is out 🌳 👉Skywork AI unveils SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized videos based on textual prompts. Code, models, & evaluation benchmark released💙 👉Review https://t.ly/MEjzL 👉Paper https://arxiv.org/pdf/2504.02436 👉Project skyworkai.github.io/skyreels-a2.github.io/ 👉Repo github.com/SkyworkAI/SkyReels-A2 🤗Models https://huggingface.co/Skywork/SkyReels-A2

17 259

💃 Video Motion Graphs 💃 👉#Adobe unveils a novel system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the system synthesizes amazing new videos. Code & Models to be released💙 👉Review https://t.ly/r4EGF 👉Paper https://lnkd.in/dK_tHyzh 👉Project https://lnkd.in/dE6c_KYZ 👉Repo TBA

17 259

🐟Segment Any Motion in Video🐟 👉From CVPR2025 a novel approach for moving object segmentation that combines DINO-based semantic features and SAM2. Code under MIT license💙 👉Review https://t.ly/4aYjJ 👉Paper arxiv.org/pdf/2503.22268 👉Project motion-seg.github.io/ 👉Repo github.com/nnanhuang/SegAnyMo

17 259

🌳MSVA Zero-Shot Multi-View🌳 👉Niantic unveils MVSA, novel Multi-View Stereo Architecture to work anywhere by generalizing across diverse domains & depth ranges. Highly accurate & 3D-consistent depths. Code & models announced💙 👉Review https://t.ly/LvuTh 👉Paper https://arxiv.org/pdf/2503.22430 👉Project https://nianticlabs.github.io/mvsanywhere/ 👉Repo https://lnkd.in/ddQz9eps

17 259

🏓LATTE-MV: #3D Table Tennis🏓 👉UC Berkeley unveils at #CVPR2025 a novel system for reconstructing monocular video of table tennis in 3D with uncertainty-aware controller that anticipates opponent actions. Code & Dataset announced, to be released💙 👉Review https://t.ly/qPMOU 👉Paper arxiv.org/pdf/2503.20936 👉Project sastry-group.github.io/LATTE-MV/ 👉Repo github.com/sastry-group/LATTE-MV

17 259

🦎 Scaling Vision to 4K🦎 👉PS3 by #Nvidia (+UC Berkeley) to scale-up CLIP-style vision pre-training to 4K with *near-constant* cost. Encoding LR global image and selectively processes only informative HR regions. Impressive work. Code/weights & 🤗 announced💙 👉Review https://t.ly/WN479 👉Paper https://lnkd.in/ddWq8UpX 👉Project https://lnkd.in/dMkTY8-k 👉Repo https://lnkd.in/d9YSB6yv

17 259

🔥 Dereflection Any Image 🔥 👉SJTU & #Huawei unveils DAI, novel diffusion-based framework able to recover from a wide range of reflection types. One-step diffusion with deterministic outputs & fast inference. Inference, pretrained models & training released💙 👉Review https://t.ly/PDA9K 👉Paper https://arxiv.org/pdf/2503.17347 👉Project abuuu122.github.io/DAI.github.io/ 👉Repo github.com/Abuuu122/Dereflection-Any-Image

17 259

🙀3D MultiModal Memory🙀 👉M3 is a novel framework by UCSD & #NVIDIA for rendering 3D scenes w/ RGB & foundation model embeddings. Rich spatial & semantic understanding via novel memory system designed to retain multimodal info through videos 👉Review https://t.ly/OrXZO 👉Paper arxiv.org/pdf/2503.16413 👉Project https://lnkd.in/dXAZ97KH 👉Repo https://lnkd.in/dWvunCET

17 259

🥎LLM Spatial Understanding🥎 👉SpatialLM by Manycore: novel LLM designed to process 3D point cloud data and generate structured 3D scene understanding outputs. Code, model & data 💙 👉Review https://t.ly/ejr1s 👉Project manycore-research.github.io/SpatialLM/ 👉Code github.com/manycore-research/SpatialLM 🤗Models https://huggingface.co/manycore-research

17 259

teaser (1) (online-video-cutter.com) (1).mp42.02 MB

17 259

🧞 IMPOSSIBLE Videos 🧞 👉IPV-Bench: counterfactual and anti-reality scenes impossible in real world. A novel challenge designed to evaluate and foster progress in video understanding and generation. Code & 🤗-Data 💙 👉Review https://t.ly/D7jhm 👉Paper arxiv.org/pdf/2503.14378 👉Project showlab.github.io/Impossible-Videos/ 👉Repo github.com/showlab/Impossible-Videos

17 259

🌱 #Py4AI: line-up is official 🌱 👉Last week we announced the first part of our incredible line-up for PY4AI 2025. It's time to disclose the second one and drive you crazy👇 𝐓𝐡𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 𝐛𝐚𝐭𝐜𝐡 𝐨𝐟 𝐬𝐩𝐞𝐚𝐤𝐞𝐫𝐬: 🔥Alfredo Canziani | New York University 🔥Fanny Bouton | OVHcloud 🔥Full list: https://t.ly/JJP8B

17 259

🧸 Occluded 3D Reconstruction 🧸 👉Oxford unveils a novel 3D generative model to reconstruct 3D objects from partial observations. Code (TBR), demo, model on HF💙 👉Review https://t.ly/Lr5D7 👉Paper arxiv.org/pdf/2503.13439 👉Project sm0kywu.github.io/Amodal3R/ 🤗huggingface.co/spaces/Sm0kyWu/Amodal3R