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 248 Tecnologías y Aplicaciones7 687...

📈 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 17 236 suscriptores, ocupando la posición 7 687 en la categoría Tecnologías y Aplicaciones y el puesto 2 248 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 236 suscriptores.

Según los últimos datos del 03 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -80, y en las últimas 24 horas de -13, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 13.16%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 271 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 14.
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 04 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.

17 236

Suscriptores

-1324 horas

-677 días

-8030 días

2 271

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

13.16%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 236

🔍 Nvidia Locate Anything 🔍 👉Diverse localization tasks under a unified vision-language model, including document understanding, GUI grounding, dense detection, and OCR. Repo released💙 👉Review https://t.ly/PvwFo 👉Paper https://lnkd.in/dWfNpzPZ 👉Project https://lnkd.in/dM89BX-8 👉Repo https://lnkd.in/dC4KCQSM

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🪔Latent Decoding with Pixel Diffusion🪔 👉PiD by Nvidia is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass. Repo under Apache 2.0💙 👉Review https://t.ly/y19mA 👉Paper https://lnkd.in/duVC25C2 👉Project https://lnkd.in/dW6TkzCB 👉Repo https://lnkd.in/dnGdgKRr

17 236

🍒Count Anything, Any Granularity🍒 👉Open-world counting as multi-grained counting, where visual exemplars specify target appearance and fine-grained text specifies the intended semantic granularity across five explicit levels. Repo/Data under Apache💙 👉Review https://t.ly/nqz80 👉Paper https://lnkd.in/dp7khTRU 👉Project https://lnkd.in/d_jfX_Yn 👉Repo https://lnkd.in/dkTRGZkG 👉Data https://lnkd.in/dB83jRyT

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🦄Unified Correspondence Transformer🦄 👉UniCorrn is the first correspondence model with shared weights that unifies 2D-2D, 2D-3D, and 3D-3D geometric matching with an end-to-end transformer architecture. Repo under CC BY-NC-SA 4.0💙 👉Review https://t.ly/2OBdq 👉Paper https://arxiv.org/pdf/2605.04044 👉Project https://neu-vi.github.io/UniCorrn/ 👉Repo https://github.com/neu-vi/UniCorrn

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17 236

🪝Syn4D: Multiview Synthetic 4D Dataset🪝 👉Syn4D is novel multi-view synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations💙 👉Review https://t.ly/SL1mk 👉Paper https://arxiv.org/pdf/2605.05207 👉Project https://jzr99.github.io/Syn4D/ 👉Repo https://github.com/jzr99/Syn4D 👉Data huggingface.co/datasets/Syn4D/Syn4D_RGBD/tree/main

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🧘‍♀️Holistic Shot Boundary Detection🧘‍♀️ 👉OmniShotCut detects shot changes of the video in diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.), and recognize Sudden Jump and Transitions (dissolve, fade, wipe, etc.) by proposing a Shot-Query-based Video Transformer. Repo, demo & benchmark💙 👉Review https://t.ly/sTi7N 👉Paper https://arxiv.org/pdf/2604.24762 👉Project uva-computer-vision-lab.github.io/OmniShotCut_website/ 👉Repo github.com/UVA-Computer-Vision-Lab/OmniShotCut

17 236

🛒 Reshoot-Anything is out 🛒 👉Reshoot-Anything reshoots dynamic monocular videos under novel camera trajectories. Code under Apache 2.0 💙 👉Review https://t.ly/MIqAc 👉Paper https://arxiv.org/pdf/2604.21776 👉Project adithyaiyer1999.github.io/reshoot-anything/ 👉Repo github.com/morphicfilms/video-to-video

17 236

💙 PY4AI 2026: here we are! 💙 👉The third edition of our conference is official! Speaker list and (free) tickets: https://t.ly/L4_52

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🎈Face Anything 4D (SOTA)🎈 👉A novel unified 4D facial reconstruction and dense tracking from image sequences: new SOTA in facial single-image and mono-video depth estimation, dense 4D reconstruction, and 3D point tracking. Repo & Dataset announced💙 👉Review https://t.ly/zItie 👉Paper https://arxiv.org/pdf/2604.19702 👉Project kocasariumut.github.io/FaceAnything 👉Repo TBA

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🌗Mobile Ultra-detailed Avatars🌗 👉Given skeletal poses and a virtual camera as inputs, MUA by Max Planck Institute produces photorealistic renderings and hyper-detailed geometry of animatable clothed humans. Repo announced💙 👉Review https://t.ly/QPCy6 👉Paper https://arxiv.org/pdf/2604.18583 👉Project https://vcai.mpi-inf.mpg.de/projects/MUA/ 👉Repo TBA

17 236

👩‍🦰 3D Head w/ Deformable Hair 👩‍🦰 👉Xi’an Jiaotong University unveils a novel method that reconstructs decoupled 3D Gaussian head avatars from a single input image: effortless hairstyle transfer with natural dynamic hair motion. Code announced💙 👉Review https://t.ly/kWZdd 👉Paper https://arxiv.org/pdf/2604.14782 👉Project yuansun-xjtu.github.io/CompHairHead.io/ 👉Repo yuansun-xjtu.github.io/CompHairHead.io/

17 236

🐞GCT 3D Reconstruction🐞 👉ANT unveils LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. Repo under A-NC 4.0 International💙 👉Review https://t.ly/ExodA 👉Paper https://arxiv.org/pdf/2604.14141 👉Project https://arxiv.org/pdf/2604.14141 👉Repo github.com/robbyant/lingbot-map

17 236

📱3D Human-Object Contact📱 👉Pi-HOC by CMU + NREC is a novel single-pass, instance-aware framework for dense 3D semantic contact prediction of all human-object pairs. Repo announced💙 👉Review https://t.ly/TAgG1 👉Paper https://arxiv.org/pdf/2604.12923 👉Project https://pi-hoc.github.io/ 👉Repo https://github.com/SravanChittupalli/Pi-HOC

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🐓Interactive Objects from EgoVideo🐓 👉EgoFun3D by Simon Fraser University is a coordinated task, dataset and benchmark for modeling interactive 3D objects from egocentric videos. Repo (TBA), demo & dataset💙 👉Review https://t.ly/YhGN7 👉Paper arxiv.org/pdf/2604.11038 👉Project 3dlg-hcvc.github.io/EgoFun3D/ 👉Repo github.com/3dlg-hcvc/EgoFun3D 👉Demo bc79fea884062374b3.gradio.live/

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🧴OmniShow: Automatic Contents Creation🧴 👉OmniShow is the novel SOTA in content creation with industry-grade performance. Impressive results, best with audio. Repo announced💙 👉Review https://t.ly/Pm-7U 👉Paper arxiv.org/pdf/2604.11804 👉Project correr-zhou.github.io/OmniShow/ 👉Repo github.com/Correr-Zhou/OmniShow

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🔥SOTA 3D Detection in the wild🔥 👉WildDet3D is a novel unified geometry-aware architecture that natively accepts text, point, and box prompts and can incorporate auxiliary depth signals at inference time. New SOTA! Repo, models & #iphone💙 👉Review https://t.ly/8NxBN 👉Paper https://arxiv.org/pdf/2604.08626 👉Project https://allenai.github.io/WildDet3D/ 👉Repo https://github.com/allenai/WildDet3D

17 236

🐞6D Object Pose w/ Deformation🐞 👉DeSOPE by Xidian & #MagicLeap is a novel large-scale dataset for 6DoF deformed objects: 665K pose annotations produced via a semiautomatic pipeline. Repo & Dataset announced💙 👉Review https://t.ly/M5VgX 👉Paper https://arxiv.org/pdf/2604.06720 👉Project https://desope-6d.github.io/ 👉Repo TBA

17 236

🪞1.1M Metric VTON Dataset🪞 👉Google's Fit-Inclusive Try-on: large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. Repo & dataset announced💙 👉Review https://t.ly/cs-pt 👉Paper arxiv.org/pdf/2604.08526 👉Project johannakarras.github.io/FIT/ 👉Repo TBA

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Here the preview, tomorrow the full clip from official source :)