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
Mostrar más📈 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
Archivo de publicaciones
🖲️ VGG Transformer 🖲️
👉VGGT by VGG & #META (#CVPR2025) is a feed-forward neural net. that directly infers all key 3D attributes of a scene within seconds. Code released💙
👉Review https://t.ly/WoWXL
👉Paper https://arxiv.org/pdf/2503.11651
👉Project https://vgg-t.github.io/
👉Code github.com/facebookresearch/vggthttps://t.ly/WoWXL
🍾 6D Tracking & Pose SOTA 🍾
👉ČVUT unveils the new SOTA in RGB 6D pose estimation and tracking. Suitable for ego-clips & 7-axis robo-manipulation. Code under MIT💙
👉Review https://t.ly/pSqFR
👉Paper arxiv.org/pdf/2503.10307
👉Code github.com/ponimatkin/freepose
🫀HyperFast Mycardium tracking🫀
👉Norwegian institutes unveil MyoTracker, a low-complexity architecture (0.3M params) for point tracking in echocardiography. Built on CoTracker2, it provides point predictions for the entire sequence in a single step. Code released under non commercial license💙
👉Review https://t.ly/6wo8q
👉Paper https://arxiv.org/pdf/2503.10431
👉Code https://github.com/artemcher/myotracker
🐶OVTR: E2E Transformer MOT🐶
👉HUST University proposes OVTR (End-to-End Open-Vocabulary Multiple Object Tracking with TRansformer), the first end-to-end open-vocabulary tracker that models motion, appearance, and category simultaneously. Source Code released under MIT💙
👉Review https://t.ly/K3ASX
👉Paper arxiv.org/pdf/2503.10616
👉Code https://github.com/jinyanglii/OVTR
🎯RexSeek: Referring Any Object🎯
👉Novel referring detection model based on multimodal LLM to precisely locate objects based on user-input natural language. Model specialization on humans. Code released 💙
👉Review https://shorturl.at/CGsT2
👉Paper arxiv.org/pdf/2503.08507
👉Code github.com/IDEA-Research/RexSeek
💙 Announcing #Py4AI 2025 💙
👉 The second edition of Py4AI conference is official! An all-day, fully free, event for #AI & #Python lovers.
𝐓𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐛𝐚𝐭𝐜𝐡 𝐨𝐟 𝐬𝐩𝐞𝐚𝐤𝐞𝐫𝐬:
🚀Dana Aubakirova | Hugging Face🤗
🚀Yunhao Liu & Ruoya Sheng | ByteDance🔥
🚀Alice Casiraghi | 🌏🌎🌍
🚀Luca Arrotta, PhD | Datapizza🍕
🚀Valeria Zuccoli | Bettini Srl
🚀Mirco Planamente | ARGO Vision
🚀Daniele Zonca | Red Hat
👉 Info & registration: https://t.ly/37wWj
📒 Moving-Camera Diffusion 📒
👉Tencent unveils TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. Impressive results, the future of commercial #adv. Code & Demo released💙
👉Review https://t.ly/L-IoR
👉Paper https://arxiv.org/pdf/2503.05638
👉Project https://trajectorycrafter.github.io/
👉Repo github.com/TrajectoryCrafter/TrajectoryCrafter
🤗Demo https://huggingface.co/spaces/Doubiiu/TrajectoryCrafter
🍎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
🔥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
🔥🔥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
🧠 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/
🏉 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
👽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
🔥 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
🌈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
🔥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
🔥 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
Hi friends, what other kind of content would you like to *OCCASIONALLY* see in this group?
🪛 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
🦶 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)
