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 7 500 en la categoría Tecnologías y Aplicaciones y el puesto 2 124 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
⚔️SAMurai: SAM for Tracking⚔️
👉UWA unveils SAMURAI, an enhanced adaptation of SAM 2 specifically designed for visual object tracking. New SOTA! Code under Apache 2.0💙
👉Review https://t.ly/yGU0P
👉Paper https://arxiv.org/pdf/2411.11922
👉Repo https://github.com/yangchris11/samurai
👉Project https://yangchris11.github.io/samurai/
🧰 EchoMimicV2: Semi-body Human 🧰
👉Alipay (ANT Group) unveils EchoMimicV2, the novel SOTA half-body human animation via APD-Harmonization. See clip with audio (ZH/ENG). Code & Demo announced💙
👉Review https://t.ly/enLxJ
👉Paper arxiv.org/pdf/2411.10061
👉Project antgroup.github.io/ai/echomimic_v2/
👉Repo-v2 github.com/antgroup/echomimic_v2
👉Repo-v1 https://github.com/antgroup/echomimic
🧶 MagicQuill: super-easy Diffusion Editing 🧶
👉MagicQuill is a novel system designed to support users in smart editing of images. Robust UI/UX (e.g., inserting/erasing objects, colors, etc.) under a multimodal LLM to anticipate user intentions in real time. Code & Demos released 💙
👉Review https://t.ly/hJyLa
👉Paper https://arxiv.org/pdf/2411.09703
👉Project https://magicquill.art/demo/
👉Repo https://github.com/magic-quill/magicquill
👉Demo https://huggingface.co/spaces/AI4Editing/MagicQuill
🛥️ Global Tracklet Association MOT 🛥️
👉A novel universal, model-agnostic method designed to refine and enhance tracklet association for single-camera MOT. Suitable for datasets such as SportsMOT, SoccerNet & similar. Source code released💙
👉Review https://t.ly/gk-yh
👉Paper https://lnkd.in/dvXQVKFw
👉Repo https://lnkd.in/dEJqiyWs
🔥 4 NanoSeconds inference 🔥
👉LogicTreeNet: convolutional differentiable logic gate net. with logic gate tree kernels: Computer Vision into differentiable LGNs. Up to 6100% smaller than SOTA, inference in 4 NANOsecs!
👉Review https://t.ly/GflOW
👉Paper https://lnkd.in/dAZQr3dW
👉Full clip https://lnkd.in/dvDJ3j-u
🐔SeedEdit: foundational T2I🐔
👉ByteDance unveils a novel T2I foundational model capable of delivering stable, high-aesthetic image edits which maintain image quality through unlimited rounds of editing instructions. No code announced but a Demo is online💙
👉Review https://t.ly/hPlnN
👉Paper https://arxiv.org/pdf/2411.06686
👉Project team.doubao.com/en/special/seededit
🤗Demo https://huggingface.co/spaces/ByteDance/SeedEdit-APP
❄️Don’t Look Twice: ViT by RLT❄️
👉CMU unveils RLT: speeding up the video transformers inspired by run-length encoding for data compression. Speed the training up and reducing the token count by up to 80%! Source Code announced 💙
👉Review https://t.ly/ccSwN
👉Paper https://lnkd.in/d6VXur_q
👉Project https://lnkd.in/d4tXwM5T
👉Repo TBA
🫠 X-Portrait 2: SOTA(?) Portrait Animation 🫠
👉ByteDance unveils a preview of X-Portrait2, the new SOTA expression encoder model that implicitly encodes every minuscule expressions from the input by training it on large-scale datasets. Impressive results but no paper & code announced.
👉Review https://t.ly/8Owh9 [UPDATE]
👉Paper ?
👉Project byteaigc.github.io/X-Portrait2/
👉Repo ?
🧠 Single Neuron Reconstruction 🧠
👉SIAT unveils NeuroFly, a framework for large-scale single neuron reconstruction. Formulating neuron reconstruction task as a 3-stage streamlined workflow: automatic segmentation - connection - manual proofreading. Bridging computer vision and neuroscience 💙
👉Review https://t.ly/Y5Xu0
👉Paper https://arxiv.org/pdf/2411.04715
👉Repo github.com/beanli161514/neurofly
💪 Muscles in Time Dataset 💪
👉Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. MinT contains 9+ hours of simulation data covering 227 subjects and 402 simulated muscle strands. Code & Dataset available soon 💙
👉Review https://t.ly/108g6
👉Paper arxiv.org/pdf/2411.00128
👉Project davidschneider.ai/mint
👉Code github.com/simplexsigil/MusclesInTime
🏣 CityGaussianV2: Large-Scale City 🏣
👉A novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency: 10x compression, 25% faster & -50% memory! Source code released💙
👉Review https://t.ly/Xgn59
👉Paper arxiv.org/pdf/2411.00771
👉Project dekuliutesla.github.io/CityGaussianV2/
👉Code github.com/DekuLiuTesla/CityGaussian
☀️ Universal Relightable Avatars ☀️
👉#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results!
👉Review https://t.ly/U-ESX
👉Paper arxiv.org/pdf/2410.24223
👉Project junxuan-li.github.io/urgca-website
☀️ Universal Relightable Avatars ☀️
👉#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results!
👉Review https://t.ly/U-ESX
👉Paper arxiv.org/pdf/2410.24223
👉Project junxuan-li.github.io/urgca-website
🍜 REM: Segment What You Describe 🍜
👉REM is a framework for segmenting concepts in video that can be described via LLM. Suitable for rare & non-object dynamic concepts, such as waves, smoke, etc. Code & Data announced 💙
👉Review https://t.ly/OyVtV
👉Paper arxiv.org/pdf/2410.23287
👉Project https://miccooper9.github.io/projects/ReferEverything/
🔥🔥 The code is out 🔥🔥
👉Code https://github.com/HaixinShi/fmov_pose
🔥 D-FINE: new SOTA Detector 🔥
👉D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR model. New SOTA on MS COCO with additional data. Code & models available 💙
👉Review https://t.ly/aw9fN
👉Paper https://arxiv.org/pdf/2410.13842
👉Code https://github.com/Peterande/D-FINE
🫐 Blendify: #Python + Blender 🫐
👉Lightweight Python framework that provides a high-level API for creating & rendering scenes with #Blender. It simplifies data augmentation & synthesis. Source Code released💙
👉Review https://t.ly/l0crA
👉Paper https://arxiv.org/pdf/2410.17858
👉Code https://virtualhumans.mpi-inf.mpg.de/blendify/
⛈️ SMITE: SEGMENT IN TIME ⛈️
👉SFU unveils SMITE: a novel AI that -with only one or few segmentation references with fine granularity- is able to segment different unseen videos respecting the segmentation references. Dataset & Code (under Apache 2.0) announced 💙
👉Review https://t.ly/w6aWJ
👉Paper arxiv.org/pdf/2410.18538
👉Project segment-me-in-time.github.io/
👉Repo github.com/alimohammadiamirhossein/smite
🌻 Plant Camouflage Detection🌻
👉PlantCamo Dataset is the first dataset for plant camouflage detection: 1,250 images with camouflage characteristics. Source Code released 💙
👉Review https://t.ly/pYFX4
👉Paper arxiv.org/pdf/2410.17598
👉Code github.com/yjybuaa/PlantCamo
🪁 PL2Map: efficient neural 2D-3D 🪁
👉PL2Map is a novel neural network tailored for efficient representation of complex point & line maps. A natural representation of 2D-3D correspondences
👉Review https://t.ly/D-bVD
👉Paper arxiv.org/pdf/2402.18011
👉Project https://thpjp.github.io/pl2map
👉Code https://github.com/ais-lab/pl2map
