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 240 Tecnologías y Aplicaciones7 726...

📈 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 17 154 suscriptores, ocupando la posición 7 726 en la categoría Tecnologías y Aplicaciones y el puesto 2 240 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 154 suscriptores.

Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -166, y en las últimas 24 horas de -6, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 23.63%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 6.86% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 4 057 visualizaciones. En el primer día suele acumular 1 177 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 22 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.

17 154

Suscriptores

-624 horas

-277 días

-16630 días

4 057

Visitas de la publicación

~ 1 17724 horas

~ 1 35348 horas

23.63%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 154

🔥 940+ FPS Multi-Person Pose Estimation 🔥 👉RTMW (Real-Time Multi-person Whole-body pose estimation models) is a series of high-performance models for 2D/3D whole-body pose estimation. Impressive 940+ FPS on #GPU. Code & models available💙 👉Review https://t.ly/XkBmg 👉Paper arxiv.org/pdf/2407.08634 👉Repo github.com/open-mmlab/mmpose/tree/main/projects/rtmpose

17 154

🍾TAPVid-3D: benchmark for TAP-3D🍾 👉#Deepmind (+College London & Oxford) introduces TAPVid-3D, a new benchmark for evaluating long-range Tracking Any Point in 3D: 4,000+ real-world videos, composed of three different data sources spanning a variety of object types, motion patterns, and indoor/outdoor environments. Data & Code available, Apache 2.0💙 👉Review https://t.ly/SsptD 👉Paper https://arxiv.org/pdf/2407.05921 👉Project https://tapvid3d.github.io/ 👉Code github.com/google-deepmind/tapnet/tree/main/tapnet/tapvid3d

17 154

🐸 Tracking Everything via Decomposition 🐸 👉Hefei unveils a novel decoupled representation that divides static scenes and dynamic objects in terms of motion and appearance. A more robust tracking through occlusions and deformations. Source Code announced under MIT License💙 👉Review https://t.ly/OsFTO 👉Paper https://arxiv.org/pdf/2407.06531 👉Repo github.com/qianduoduolr/DecoMotion

17 154

🤖 CODERS: Stereo Detection, 6D & Shape 🤖 👉CODERS: one-stage approach for Category-level Object Detection, pose Estimation and Reconstruction from Stereo images. Source Code announced💙 👉Review https://t.ly/Xpizz 👉Paper https://lnkd.in/dr5ZxC46 👉Repo (TBA)

17 154

🔥 Segment Any 4D Gaussians 🔥 👉SA4G is a novel framework to segment anything in #4D Gaussians world. HQ segmentation within seconds in 4D Gaussians and remove, recolor, compose, and render HQ anything masks. Source Code available within August 2024💙 👉Review https://t.ly/uw3FS 👉Paper https://arxiv.org/pdf/2407.04504 👉Project https://jsxzs.github.io/sa4d/ 👉Repo https://github.com/hustvl/SA4D

17 154

🪴 CAVIS: SOTA Context-Aware Segmentation🪴 👉DGIST unveils the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. It's the new SOTA in several benchmarks. Source Code announced💙 👉Review https://t.ly/G5obN 👉Paper arxiv.org/pdf/2407.03010 👉Repo github.com/Seung-Hun-Lee/CAVIS 👉Project seung-hun-lee.github.io/projects/CAVIS

17 154

🪩 MimicMotion: HQ Motion Generation 🪩 👉#Tencent opens a novel controllable video generation framework, dubbed MimicMotion, which can generate HQ videos of arbitrary length mimicking specific motion guidance. Source Code available💙 👉Review https://t.ly/XFoin 👉Paper arxiv.org/pdf/2406.19680 👉Project https://lnkd.in/eW-CMg_C 👉Code https://lnkd.in/eZ6SC2bc

17 154

🪅🪅Anomaly Object-Detection🪅🪅 👉The University of Edinburgh introduces a novel anomaly detection problem that focuses on identifying ‘odd-looking’ objects relative to the other instances within a multiple-views scene. Code announced💙 👉Review https://t.ly/3dGHp 👉Paper arxiv.org/pdf/2406.20099 👉Repo https://lnkd.in/d9x6FpUq

17 154

🔥 Depth Anything v2 is out! 🔥 👉 Depth Anything V2: outperforming V1 in robustness and fine-grained details. Trained from 595K synthetic labeled images and 62M+ real unlabeled images, the new SOTA in monocular depth estimation (MDE). Code & Models available💙 👉Review https://t.ly/QX9Nu 👉Paper arxiv.org/pdf/2406.09414 👉Project depth-anything-v2.github.io/ 👉Repo github.com/DepthAnything/Depth-Anything-V2 👉Data huggingface.co/datasets/depth-anything/DA-2K

17 154

🌾 LLaNA: a NeRF-LLM assistant 🌾 👉UniBO unveils LLaNA; novel Multimodal-LLM that understands and reasons on an input NeRF. It processes directly the NeRF weights and performs tasks such as captioning, Q&A, & zero-shot classification of NeRFs. 👉Review https://t.ly/JAfhV 👉Paper arxiv.org/pdf/2406.11840 👉Project andreamaduzzi.github.io/llana/ 👉Code & Data coming

17 154

🌮 MeshAnything with Transformers 🌮 👉MeshAnything converts any 3D representation into Artist-Created Meshes (AMs), i.e., meshes created by human artists. It can be combined with various 3D asset production pipelines, such as 3D reconstruction and generation, to transform their results into AMs that can be seamlessly applied in the 3D industry. Source Code available💙 #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Review https://t.ly/HvkD4 👉Paper arxiv.org/pdf/2406.10163 👉Code github.com/buaacyw/MeshAnythinghttps://t.ly/HvkD4

17 154

🍦Geometry Guided Depth Estimation🍦 👉A novel system for depth estimation and #3D reconstruction which can take as input, where available, previously-made estimates of the scene’s geometry 👉Review https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2406.18387 👉Repo (empty) https://github.com/nianticlabs/DoubleTake

17 154

🍦Geometry Guided Depth Estimation🍦 👉#Niantic (+ULC) unveils a novel system for depth estimation and #3D reconstruction which can take as input, where available, previously-made estimates of the scene’s geometry. Source Code announced💙 👉Review https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2406.18387 👉Repo (empty) https://github.com/nianticlabs/DoubleTake

17 154

🐻StableNormal: Stable/Sharp Normal🐻 👉Alibaba unveils StableNormal, a novel method which tailors the diffusion priors for monocular normal estimation. Hugging Face demo is available💙 👉Review https://t.ly/FPJlG 👉Paper https://arxiv.org/pdf/2406.16864 👉Demo https://huggingface.co/Stable-X

17 154

🧬 Event-driven SuperResolution 🧬 👉USTC unveils EvTexture, the first VSR method that utilizes event signals for texture enhancement. It leverages high-frequency details of events to better recover texture regions in VSR. Source Code available💙 👉Review https://t.ly/zlb4c 👉Paper arxiv.org/pdf/2406.13457 👉Code github.com/DachunKai/EvTexture

17 154

🤓 Glasses-Removal from Videos🤓 👉Lightricks unveils a novel method able to receive an input video of a person wearing glasses, and consistently removes the glasses, while preserving the ID. It works even when there are reflections, heavy makeup, and eye blinks. Code announced, not yet released💙 👉Review https://t.ly/Hgs2d 👉Paper arxiv.org/pdf/2406.14510 👉Project https://v-lasik.github.io/ 👉Code github.com/v-lasik/v-lasik-code

17 154

🌱 TokenHMR : new 3D human pose SOTA 🌱 👉TokenHMR is the new SOTA HPS method mixing 2D keypoints and 3D pose accuracy, thus leveraging Internet data without known camera parameters. It's the new SOTA by a large margin. 👉Review https://t.ly/K9_8n 👉Paper arxiv.org/pdf/2404.16752 👉Project tokenhmr.is.tue.mpg.de/ 👉Code github.com/saidwivedi/TokenHMR

17 154

💦 Self-driving in wet conditions 💦 👉BMW SemanticSpray: novel dataset contains scenes in wet surface conditions captured by camera, LiDAR and radar. Camera: 2D Boxes | LiDAR: 3D Boxes, Semantic Labels | Radar: Semantic Labels. 👉Review https://t.ly/8S93j 👉Paper https://lnkd.in/dnN5MCZC 👉Project https://lnkd.in/dkUaxyEF 👉Data https://lnkd.in/ddhkyXv8

17 154

🧤HOT3D Hand/Object Tracking🧤 👉#Meta opens a novel egocentric dataset for 3D hand & object tracking. A new benchmark for vision-based understanding of 3D hand-object interactions. Dataset available 💙 👉Review https://t.ly/cD76F 👉Paper https://lnkd.in/e6_7UNny 👉Data https://lnkd.in/e6P-sQFK

17 154

🌵 RobustSAM for Degraded Images 🌵 👉RobustSAM, the evolution of SAM for degraded images; enhancing the SAM’s performance on low-quality images while preserving prompt-ability & zeroshot generalization. Dataset & Source Code released💙 👉Review https://t.ly/mnyyG 👉Paper arxiv.org/pdf/2406.09627 👉Project robustsam.github.io 👉Code github.com/robustsam/RobustSAM