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 234 Tecnologías y Aplicaciones7 718...

📈 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 17 166 suscriptores, ocupando la posición 7 718 en la categoría Tecnologías y Aplicaciones y el puesto 2 234 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 166 suscriptores.

Según los últimos datos del 20 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -169, y en las últimas 24 horas de 0, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 22.86%. 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 3 926 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 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 21 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.

17 166

Suscriptores

Sin datos24 horas

-357 días

-16930 días

3 926

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

22.86%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 157

🍎FindTrack: text-driven VOS 🍎 👉Yonsei University introduces FindTrack, a novel decoupled framework that separates text-driven target ID from mask propagation. Impressive results (even under severe occlusions), new SOTA. Source Code & models to be released💙 👉Review https://t.ly/2smaF 👉Paper arxiv.org/pdf/2503.03492 👉Repo github.com/suhwan-cho/FindTrack

17 157

🔥Distill-Any-Depth: SOTA MDE🔥 👉Distill-Any-Depth is the new SOTA monocular depth estimation model trained with a novel knowledge distillation. Authors: ZJUT, WestLake University, LZU & NTU. Source Code, pre-trained models & HF-demo released💙 👉Review https://t.ly/GBJgi 👉Paper arxiv.org/pdf/2502.19204 👉Repo https://lnkd.in/dPtxNrQh 🤗Demo https://lnkd.in/d2TMPf4b

17 157

🔥🔥Distill-Any-Depth: new SOTA MDE🔥🔥 👉Distill-Any-Depth is the new SOTA monocular depth estimation model trained with a novel knowledge distillation. Source Code, pre-trained models & f-demo released💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Authors: ZJUT, WestLake University, LZU & NTU ✅Multiple D-normalization on pseudo-label distillation ✅Proposing novel Cross-Context Distillation approach ✅Introducing new multi-teacher distillation framework ✅Pre-trained Models and code released under MIT hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse hashtag#LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2502.19204 👉Repo https://lnkd.in/dPtxNrQh 🤗Demo https://lnkd.in/d2TMPf4b

17 157

🧠 Distractor-Aware SAM2 🧠 👉A novel distractor-aware memory for SAM2 and an introspection-based update strategy for VOT. Code & Dataset released💙 👉Review https://t.ly/RBRpQ 👉Paper arxiv.org/pdf/2411.17576 👉Project jovanavidenovic.github.io/dam-4-sam 👉Repo github.com/jovanavidenovic/DAM4SAM/

17 157

🏉 MITracker: Multi-View Tracking 🏉 👉ShangaiTech unveils MITracker, a novel Multi-View Integration Tracker, to efficiently integrate multi-view object features and provide stable tracking outcomes. Code & Dataset to be released💙 👉Review https://t.ly/RTNUo 👉Paper https://arxiv.org/pdf/2502.20111 👉Project https://xum007.github.io/MITracker.github.io/ 👉Repo https://github.com/XuM007/MITracker

17 157

👽Neural-Free Sparse Voxels Rasterization👽 👉#Nvidia unveils a novel efficient radiance field rendering algorithm that incorporates a rasterization process on adaptive sparse voxels without neural networks or 3D Gaussians. Code released (custom license)💙 👉Review https://t.ly/Nh_ic 👉Paper https://lnkd.in/g8k8Zs6R 👉Project https://lnkd.in/gR-bD4Wx 👉Repo https://lnkd.in/gNHX-w4t

17 157

🔥 YOLOv12 is out (new SOTA) 🔥 👉YOLOv12 is a novel attention-centric YOLO framework that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms. Source Code & Demo released💙 👉Review https://t.ly/jj1oR 👉Paper https://arxiv.org/pdf/2502.12524 👉Repo https://github.com/sunsmarterjie/yolov12 🤗 https://huggingface.co/spaces/sunsmarterjieleaf/yolov12

17 157

🌈L4P: Unified Low-Level 4D Vision🌈 👉#Nvidia L4P is a novel feedforward, general-purpose, architecture to solve low-level 4D perception tasks in a unified framework. L4P combines a ViTbased backbone with per-task heads that are lightweight and therefore do not require extensive training. One backbone - many SOTAs. Code announced 💙 👉Review https://t.ly/04DGj 👉Paper arxiv.org/pdf/2502.13078 👉Project research.nvidia.com/labs/lpr/l4p/ 👉Repo TBA

17 157

🔥Large Language DIFFUSION Model🔥 👉Renmin University introduces LLaDA, a *diffusion model* trained entirely from scratch, rivaling LLaMA3 8B in performance. Pre-trained from scratch on 2.3T tokens using 0.13M H800 GPU hours, followed by SFT on 4.5M pairs. A new paradigm is born? Repo by the end of Feb.25 💙 👉Review https://t.ly/7Cnrh 👉Paper https://lnkd.in/dCWi3byk 👉Project https://lnkd.in/dB7JRYeA 👉Repo https://lnkd.in/dAqzeCHJ

17 157

🔥 Animate Anyone 2 🔥 👉 The evolution of the first version that enables character animation w/ environment affordance. Amazing results but no code announced 🥲 👉Review https://t.ly/iNNLB 👉Paper https://arxiv.org/pdf/2502.06145 👉Project https://humanaigc.github.io/animate-anyone-2

17 157

Hi friends, what other kind of content would you like to *OCCASIONALLY* see in this group?

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17 157

🪛 Make anything "Rig-Ready" 🪛 👉RigAnything is a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints, skeleton topologies, and assigning skinning weights in a template-free manner. Online demo announced💙 👉Review https://t.ly/bNwxq 👉Paper arxiv.org/pdf/2502.09615 👉Project www.liuisabella.com/RigAnything

17 157

🦶 It's all About Foot 🦶 👉 A collection of three works all about human foot: synthetic foot renders, reconstruction and surface normals. Repos & Datasets available💙 👉Review https://t.ly/GY8mL 👉Paper (last) arxiv.org/pdf/2502.06367 👉Projects www.ollieboyne.com/ 👉Repo github.com/OllieBoyne/FOUND 👉Repo github.com/OllieBoyne/SynFoot 👉Repo github.com/OllieBoyne/FOCUS (coming)

17 157

🥛HAMSTER: Hierarchical VLA Manipulation🥛 👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙 👉Review https://t.ly/2yXaY 👉Paper https://arxiv.org/pdf/2502.05485 👉Project https://hamster-robot.github.io/ 👉Repo TBA

17 157

🥛🥛HAMSTER: Hierarchical VLA Manipulation🥛🥛 👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Hier. Action Models w/ SeparaTEd Path Represent. ✅Fine-tuned VLMs -> to low-level 3D policy models ✅A fully open-sourced enabler for VLM-action models ✅Abundant OOD data for improving real-world control #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2502.05485 👉Project https://hamster-robot.github.io/ 👉Repo TBA

17 157

🔮Flow-Based Foundation GenAI🔮 👉Goku is the novel SOTA family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. Amazing results! Repo released (now, empty)💙 👉Review https://t.ly/dzi0O 👉Paper http://arxiv.org/pdf/2502.04896 👉Project saiyan-world.github.io/goku/ 👉Repo github.com/Saiyan-World/goku

17 157

💃HumanDiT Long-form Human💃 👉HumanDiT is a novel pose-guided Diffusion trained on a large and wild dataset w/ 14,000 hours of HQ video to produce HD videos with fine-grained bodies. Stunning results but no code announced🥲 👉Review https://t.ly/7rTRr 👉Paper https://arxiv.org/pdf/2502.04847 👉Project https://agnjason.github.io/HumanDiT-page/

17 157

🤖 META Human-Robot 🤖 👉#META PARTNR: novel benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. The largest benchmark of its kind: 100,000+ natural language tasks, spanning 60 houses and 5,819 unique objects. Code & Data (🤗) under MIT💙 👉Review https://t.ly/zcN0K 👉Paper arxiv.org/pdf/2411.00081 👉Repo github.com/facebookresearch/partnr-planner 🤗Data huggingface.co/datasets/ai-habitat/partnr_episodes

17 157

👗3D Dynamic Garments👗 👉UCLA introduces Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image. 👉Review https://t.ly/qciHV 👉Paper arxiv.org/pdf/2502.03449 👉Project dress-1-to-3.github.io

17 157

🔥 VideoJAM: #META's Video-Model (SOTA) 🔥 👉#META's VideoJAM: the new SOTA (by large margin) in motion coherence for video generation, much better than SORA! A strong motion prior into any video-gen model. Impressive results, no code announced🥲 👉Review https://shorturl.at/id7Bt 👉Paper https://arxiv.org/pdf/2502.02492 👉Project https://hila-chefer.github.io/videojam-paper.github.io/