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 17 262 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 17 262 suscriptores.
Según los últimos datos del 23 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 255, 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 18.93%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.17% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 267 visualizaciones. En el primer día suele acumular 1 237 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 20.
- 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 24 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.
17 262
Suscriptores
+224 horas
+67 días
+25530 días
Archivo de publicaciones
👉 A proof I'm not a bot...
My (short) interview to one of the biggest Italian media: AI in 2016, HPC / Quantum and how I created my startup: https://www.linkedin.com/posts/visionarynet_ai-itw25-ai-activity-7381215486115643392-t7an
Thanks for the support (and of course a new paper coming in a few hours)
🎷🎷 Clink! Chop! Thud! 🎷🎷
👉Sounding Object Detection: while an environment may contain many objects, only a few are directly involved in producing sound during an interaction. This model detects the sounding object given a video of an object interaction. Code/Data announced💙
👉Review https://t.ly/VK_1h
👉Paper https://lnkd.in/depNjVXm
👉Project https://lnkd.in/dF63EZFG
👉Repo TBA
🔩Code-Centric Agentic Education🔩
👉Show Lab unveils Code2Video: agentic, code-centric framework that generates HQ educational videos from knowledge points. Unlike pixel-based text-to-video models, this approach leverages executable Manim code to ensure clarity, coherence & reproducibility. Repo under MIT💙
👉Review https://t.ly/Fv4LJ
👉Paper https://arxiv.org/pdf/2510.01174
👉Repo https://github.com/showlab/Code2Video/
👉Project https://showlab.github.io/Code2Video/
👩🦱Physical-Hair Diffusion👩🦱
👉CONTROLHAIR is novel hybrid framework that integrates a physics simulator with conditional video diffusion to enable controllable dynamic hair rendering. Repo announced💙
👉Review https://t.ly/78LHr
👉Paper https://lnkd.in/epm-A9Fq
👉Project https://lnkd.in/evsjz298
👉Repo TBA
👔 Universal Image Restoration 👔
👉LucidFlux by HKUSTGZ is the universal image restoration framework built on a large-scale diffusion transformer that delivers photorealistic restorations of real-world low-quality (LQ) images, outperforming SOTA diffusion-based models across diverse degradations. Repo under custom Non-Commercial License💙
👉Review https://t.ly/Z5cA3
👉Paper https://arxiv.org/pdf/2509.22414
👉Project https://w2genai-lab.github.io/LucidFlux/
👉Repo https://github.com/W2GenAI-Lab/LucidFlux
🤖 Real-time Interactive Long Video 🤖
👉LONGLIVE by #Nvidia is a frame-level autoregressive framework for real-time & interactive long video generation. LONGLIVE accepts sequential user prompts and generates corresponding videos in real time. Repo under non-commercial license💙
👉Review https://t.ly/jJkdY
👉Paper arxiv.org/pdf/2509.22622
👉Project nvlabs.github.io/LongLive/
👉Repo github.com/NVlabs/LongLive
🤗huggingface.co/Efficient-Large-Model/LongLive-1.3B
🔥SOTA Detection w/ DINOv3🔥
👉DEIMv2 is the evolution of DEIM framework while leveraging DINOv3. Various model sizes, from an ultra-light version up to S, M, L, & X for a wide range of scenarios. Across these variants, DEIMv2 achieves SOTA. Repo Apache2.0💙
👉Review https://t.ly/P7jEH
👉Paper arxiv.org/pdf/2509.20787
👉Repo github.com/Intellindust-AI-Lab/DEIMv2
👉Project intellindust-ai-lab.github.io/projects/DEIMv2
🤬Music vs. Face Recognition🤬
👉At their recent concerts, Massive Attack turned surveillance into art: crafting a haunting audiovisual show that exposes the dangers of facial recognition tech. Audiences were scanned with mock surveillance, confronting them with the intrusive power of #AI in real time
👉Review https://t.ly/VMrPC
👉News https://t.ly/aj1an
A few “leaks” for you from the #Nvidia presentation I’m right now. Impressive stuff.
Ps: sorry for the shitty pics ♥️
🌀 CLOPS: Vision-Driven Avatar 🌀
👉CLOPS is the first human avatar solely uses egocentric vision to perceive its surroundings and navigate. CLOPS is able to realistically move in a scene and use egocentric vision in order to find a goal by combining a data driven low level motion prior with a Q-Learning policy. A loop of visual perception & motion. Code announced💙
👉Review https://t.ly/RXp64
👉Paper https://arxiv.org/pdf/2509.19259
👉Project markos-diomataris.github.io/projects/clops/
👉Repo TBA
🏆 MOSEv2 Challenge 2025 Winner 🏆
👉A practical solution for complex segmentation based on the Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. Repo under Apache 2.0💙
👉Review https://t.ly/2MjNm
👉Paper https://arxiv.org/pdf/2509.19183
👉Paper (SeC) https://arxiv.org/pdf/2507.15852
👉Project https://rookiexiong7.github.io/projects/SeC/
👉Repo https://github.com/OpenIXCLab/SeC
🫓 WINNER of LSVOS Challenge 🫓
👉SaSaSa2VA introduces Segmentation Augmentation to improve global video understanding while remaining efficient, and employs Selective Averaging at inference to robustly fuse complementary predictions. This approach achieves SOTA on the 7th LSVOS Challenge (RVOS track). A practical solution with full repo under Apache💙
👉Review https://t.ly/aH4mB
👉Paper https://arxiv.org/pdf/2509.16972
👉Repo https://github.com/magic-research/Sa2VA
🐳 Invariant Saliency Detection 🐳
👉SI-SOD: invariant salient object detection is a paper investigating a fundamental yet underexplored issue in Salient Object Detection (SOD): the size-invariant property for evaluation protocols, particularly in scenarios when multiple salient objects of significantly different sizes appear within a single image. Repo released💙
👉Review https://lnkd.in/p/dZBfbSsf
👉Paper https://arxiv.org/pdf/2509.15573
👉Project https://ferry-li.github.io/SI_SOD/
👉Repo https://github.com/Ferry-Li/SI-SOD
🔥🔥 It's time to decide whether you want to give LinkedIn your data for AI training or not 🔥🔥
Set here: http://linkedin.com/mypreferences/d/settings/data-for-ai-improvement
