AI with Papers - Artificial Intelligence & Deep Learning
Ir al canal en Telegram
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 17 259 suscriptores, ocupando la posición 7 509 en la categoría Tecnologías y Aplicaciones y el puesto 2 130 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 259 suscriptores.
Según los últimos datos del 25 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 261, 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 16.91%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.04% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 918 visualizaciones. En el primer día suele acumular 1 216 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 26 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.
17 259
Suscriptores
+224 horas
+17 días
+26130 días
Archivo de publicaciones
🎭New 2D Landmarks SOTA🎭
👉Flawless AI unveils FaceLift, a novel semi-supervised approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment, with no need for 3D landmark datasets. No code announced🥹
👉Review https://t.ly/lew9a
👉Paper arxiv.org/pdf/2405.19646
👉Project davidcferman.github.io/FaceLift
🧤 Transformer-based 4D Hands 🧤
👉4DHands is a novel and robust approach to recovering interactive hand meshes and their relative movement from monocular inputs. Authors: Beijing Normal University, Tsinghua & #Lenovo. No code announced yet 😢
👉Review https://t.ly/wvG-l
👉Paper arxiv.org/pdf/2405.20330
👉Project 4dhands.github.io/
🪰 Dynamic Gaussian Fusion via 4D Motion Scaffolds 🪰
👉MoSca is a novel 4D Motion Scaffolds to reconstruct/synthesize novel views of dynamic scenes from monocular videos in the wild!
👉Review https://t.ly/nSdEL
👉Paper arxiv.org/pdf/2405.17421
👉Code github.com/JiahuiLei/MoSca
👉Project https://lnkd.in/dkjMVcqZ
🦓 Z.S. Diffusive Segmentation 🦓
👉KAUST (+MPI) announced the first zero-shot approach for Video Semantic Segmentation (VSS) based on pre-trained diffusion models. Source Code released under MIT💙
👉Review https://t.ly/v_64K
👉Paper arxiv.org/pdf/2405.16947
👉Project https://lnkd.in/dcSt4dQx
👉Code https://lnkd.in/dcZfM8F3
⛈️Unsupervised Neuromorphic Motion⛈️⛈️
👉The Western Sydney University unveils a novel unsupervised event-based motion segmentation algorithm, employing the #Prophesee Gen4 HD event camera.
👉Review https://t.ly/UZzIZ
👉Paper https://arxiv.org/pdf/2405.15209
👉Project https://samiarja.github.io/evairborne/
👉Repo (empty) https://github.com/samiarja/ev/_deep/_motion_segmentation
🔥 YOLOv10 Object Detector is out 🔥
👉YOLOv10: novel real-time end-to-end object detection. Code released under GNU GPL v3.0💙
👉Review https://shorturl.at/ZIHBh
👉Paper arxiv.org/pdf/2405.14458
👉Code https://github.com/THU-MIG/yolov10/
🍀 OmniGlue: Foundation Matcher 🍀
👉#Google OmniGlue from #CVPR24: the first learnable image matcher powered by foundation models. Impressive out-of-domain results!
👉Review https://t.ly/ezaIc
👉Paper https://arxiv.org/pdf/2405.12979
👉Project hwjiang1510.github.io/OmniGlue/
👉Code https://github.com/google-research/omniglue/
👚 ViViD: Diffusion Virtual Try-ON 👚
👉ViViD is a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Code announced, not released yet😢
👉Review https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2405.11794
👉Repo https://lnkd.in/dT4_bzPw
👉Project https://lnkd.in/dCK5ug4v
🖼️ Diffusive Images that Sound 🖼️
👉The University of Michigan unveils a diffusion model able to generate spectrograms that look like images but can also be played as sound.
👉Review https://t.ly/ADtYM
👉Paper arxiv.org/pdf/2405.12221
👉Project ificl.github.io/images-that-sound
👉Code github.com/IFICL/images-that-sound
⚽ 3D Shot Posture from Broadcast ⚽
👉Nagoya Univeristy unveils 3DSP soccer broadcast videos, the most extensive sports image dataset with 2D pose annotations EVER. Data and Source Code released💙
👉Review https://t.ly/IIMeZ
👉Paper https://arxiv.org/pdf/2405.12070
👉Code https://github.com/calvinyeungck/3D-Shot-Posture-Dataset/tree/master
🦕 Grounding DINO 1.5 Pro/Edge 🦕
👉Grounding DINO 1.5, a suite of advanced open-set object detection models to advanced the "Edge" of open-set object detection. Source Code released under Apache 2.0💙
👉Review https://t.ly/kS-og
👉Paper https://lnkd.in/dNakMge2
👉Code https://lnkd.in/djhnQmrm
🫀 EchoTracker: Tracking Echocardiography🫀
👉NTNU unveils EchoTracker, a two-fold coarse-to-fine model that facilitates the tracking of queried points on a tissue surface across ultrasound. Source Code released💙
👉Review https://t.ly/NyBe0
👉Paper https://arxiv.org/pdf/2405.08587
👉Code https://github.com/riponazad/echotracker/
🔥EfficientTrain++: Efficient Foundation Visual Backbone Training🔥
👉Tsinghua unveils EfficientTrain++, a simple, general, surprisingly effective, off-the-shelf approach to reduce the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer). Up to 3.0× faster on ImageNet-1K/22K without sacrificing accuracy. Source Code released 💙
👉Review https://t.ly/D8ttv
👉Paper https://arxiv.org/pdf/2405.08768
👉Code https://github.com/LeapLabTHU/EfficientTrain
🪬UHM: Authentic Hand by Phone🪬
👉 META unveils UHM, novel 3D high-fidelity avatarization of your (yes, the your one) hand. Adaptation pipeline fits the pre-trained UHM via phone scan. Source Code released 💙
👉Review https://t.ly/fU5rA
👉Paper https://lnkd.in/dyGaiAnq
👉Code https://lnkd.in/d9B_XFAA
👻 3D Humans Motion from Text 👻
👉Zhejiang (+ANT) unveils a novel method to generate human motions containing accurate human-object interactions in 3D scenes based on textural descriptions. Code announced, coming 💙
👉Review https://t.ly/eOZnU
👉Paper https://arxiv.org/pdf/2405.07784
👉Project https://zju3dv.github.io/text_scene_motion/
🐏 AniTalker: Universal Self-Talking Humans 🐏
👉SJTU (+AISpeech) unveils AniTalker, a framework that transforms a single static portrait and input audio into animated talking videos with naturally flowing movements.
👉Review https://t.ly/MD4yX
👉Paper https://arxiv.org/pdf/2405.03121
👉Project https://x-lance.github.io/AniTalker/
👉Repo https://github.com/X-LANCE/AniTalker
💥FeatUp: Model at Any Resolution💥
👉FeatUp is a task- and model-agnostic framework to restore lost spatial information in deep features. It outperforms other feature upsampling in class activation map generation, transfer learning for segmentation and depth prediction, and end-to-end training for semantic segm. Source Code released💙
👉Review https://t.ly/Evq_g
👉Paper https://lnkd.in/gweaN4s6
👉Project https://lnkd.in/gWcGXdxt
👉Code https://lnkd.in/gweq5NY4
🔫 Free-Moving Reconstruction 🔫
👉EPFL (+#MagicLeap) unveils a novel approach for reconstructing free-moving object from monocular RGB clip. Free interaction with objects in front of a moving cam without relying on any prior, and optimizes the sequence globally without any segments. Great but no code announced🥺
👉Review https://t.ly/2xhtj
👉Paper arxiv.org/pdf/2405.05858
👉Project haixinshi.github.io/fmov/
🦑 Hyper-Detailed Image Descriptions 🦑
👉#Google unveils ImageInWords (IIW), a carefully designed HIL annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process
👉Review https://t.ly/engkl
👉Paper https://arxiv.org/pdf/2405.02793
👉Repo https://github.com/google/imageinwords
👉Project https://google.github.io/imageinwords
👉Dataset huggingface.co/datasets/google/imageinwords
👉Explorer huggingface.co/spaces/google/imageinwords-explorer
🍏 XFeat: Neural Features Matching 🍏
👉XFeat (Accelerated Features) is lightweight/accurate architecture for efficient visual correspondence. It revisits fundamental design choices in CNN for detecting, extracting & matching local features
👉Review https://t.ly/ppb38
👉Paper arxiv.org/pdf/2404.19174
👉Code https://lnkd.in/dFzTpzN8
👉Project https://lnkd.in/d8JnV-iu
