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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
Больше📈 Аналитический обзор Telegram-канала AI with Papers - Artificial Intelligence & Deep Learning
Канал AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 17 168 подписчиков, занимая 7 718 место в категории Технологии и приложения и 2 234 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 168 подписчиков.
Согласно последним данным от 20 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -169, а за последние 24 часа — 0, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 22.86%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 926 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 26.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как framework, object, dataset, tba, depth.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 21 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
17 168
Подписчики
Нет данных24 часа
-357 дней
-16930 день
Архив постов
🔥 Diffusion Model <-> Depth 🔥
👉ETH & CMU on how to turn a single-image latent diffusion model (LDM) into the SOTA video depth estimator: video depth without video models. Repo released under Apache 2.0 and HF demo available💙
👉Review https://t.ly/sP9ma
👉Paper arxiv.org/pdf/2411.19189
👉Project rollingdepth.github.io/
👉Repo github.com/prs-eth/rollingdepth
🤗Demo huggingface.co/spaces/prs-eth/rollingdepthhttps://t.ly/sP9ma
🔥 S3MOT: SOTA 3D MOT 🔥
👉Wuhan University unveils S3MOT, a Selective-State-Space model-based MOT that efficiently infers 3D motion and object associations from 2D images through three core components. New SOTA on KITTI with 76.86 HOTA at 31 FPS! Code & Weights to be released under MIT license💙
👉Review https://t.ly/H_JPv
👉Paper https://arxiv.org/pdf/2504.18068
👉Repo https://github.com/bytepioneerX/s3mot
🍏#Nvidia Dynamic Pose 🍏
👉Nvidia unveils DynPose-100K, the largest dataset of dynamic Internet videos annotated with camera poses. Dataset released under Nvidia license💙
👉Review https://t.ly/wrcb0
👉Paper https://lnkd.in/dycGjAyy
👉Project https://lnkd.in/dDZ2Ej_Q
🤗Data https://lnkd.in/d8yUSB7m
🌼SOTA Textured 3D-Guided VTON🌼
👉#ALIBABA unveils 3DV-TON, a novel diffusion model for HQ and temporally consistent video. Generating animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. Code & benchmark to be released💙
👉Review https://t.ly/0tjdC
👉Paper https://lnkd.in/dFseYSXz
👉Project https://lnkd.in/djtqzrzs
👉Repo TBA
📍Moving Points -> Depth📍
👉KAIST & Adobe propose Seurat, a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories (via off-the-shelf point tracking models). Repo & Demo to be released💙
👉Review https://t.ly/qA2P5
👉Paper https://lnkd.in/dpXDaQtM
👉Project https://lnkd.in/d9qWYsjP
👉Repo https://lnkd.in/dZEMDiJh
🦧 #Nvidia Describe Anything 🦧
👉Nvidia unveils Describe Anything Model (DAM) the new SOTA in generating detailed descriptions for user-specified regions in images/videos, marked by points, boxes, scribbles, or masks. Repo under Apache, Dataset available and live demo on 🤗
👉Review https://t.ly/la4JD
👉Paper https://lnkd.in/dZh82xtV
👉Project https://lnkd.in/dcv9V2ZF
👉Repo https://lnkd.in/dJB9Ehtb
🤗Demo https://lnkd.in/dXDb2MWU
🧊TAP in Persistent 3D Geometry🧊
👉TAPIP3D is the novel SOTA for long-term 3D point tracking in mono-RGB/RGB-D. Videos as camera-stabilized spatio-temporal feature clouds, leveraging depth & motion to lift 2D video feats into a 3D world space where camera motion is effectively canceled. Code under Apache💙
👉Review https://t.ly/oooMy
👉Paper https://lnkd.in/d8uqjdE4
👉Project https://tapip3d.github.io/
👉Repo https://lnkd.in/dsvHP_8u
🔥 #Apple Co-Motion is out! 🔥
👉Apple unveils a novel approach for detecting & tracking detailed 3D poses of multiple people from single monocular stream. Temporally coherent predictions in crowded scenes with hard poses & occlusions. New SOTA, 10x faster! Code & Models released only for research💙
👉Review https://t.ly/-86CO
👉Paper https://lnkd.in/dQsVGY7q
👉Repo https://lnkd.in/dh7j7N89
🔍Event Blurry Super-Resolution🔍
👉USTC unveils Ev-DeblurVSR: event signals into BVSR for a novel event-enhanced network. Blurry Video Super-Resolution (BVSR) aiming at generating HR videos from low-resolution and blurry inputs. Pretrained models and test released under Apache💙
👉Review https://t.ly/x6hRs
👉Paper https://lnkd.in/dzbkCJMh
👉Repo https://lnkd.in/dmvsc-yS
🔥General attention-based object🔥
👉GATE3D is a novel framework designed specifically for generalized monocular 3D object detection via weak supervision. GATE3D effectively bridges domain gaps by employing consistency losses between 2D and 3D predictions.
👉Review https://t.ly/O7wqH
👉Paper https://lnkd.in/dc5VTUj9
👉Project https://lnkd.in/dzrt-qQV
🐯UniAnimate-DiT: Human Animation🐯
👉UniAnimate-DiT is a novel n' effective framework based on Wan2.1 for consistent human image animation. LoRAs to finetune the model parameters -reducing memory- maintaining the original model’s generative skills. Training and inference code released💙
👉Review https://t.ly/1I50N
👉Paper https://arxiv.org/pdf/2504.11289
👉Repo https://github.com/ali-vilab/UniAnimate-DiT
🍏PartField #3D Part Segmentation🍏
👉#Nvidia unveils PartField, a FFW approach for learning part-based 3D features, which captures the general concept of parts and their hierarchy. Suitable for single-shape decomposition, co-segm., correspondence & more. Code & Models released under Nvidia License💙
👉Review https://t.ly/fGb2O
👉Paper https://lnkd.in/dGeyKSzG
👉Code https://lnkd.in/dbe57XGH
👉Project https://lnkd.in/dhEgf7X2
🍄 4D Mocap Human-Object 🍄
👉#Adobe unveils HUMOTO, HQ dataset of human-object interactions for motion generation, computer vision, and robotics: 700+ sequences (7,875 seconds @ 30FPS), interactions with 63 precisely modeled objects and 72 articulated parts
👉Review https://t.ly/lCof3
👉Paper https://lnkd.in/dVVBDd_c
👉Project https://lnkd.in/dwBcseDf
💥Geo4D: VideoGen 4D Scene💥
👉The Oxford VGG unveils Geo4D: video diffusion for monocular 4D reconstruction. Only synthetic data for training, but strong generalization to real world: point maps, depth & ray maps for the new SOTA in dynamic reconstruction. Code released💙
👉Review https://t.ly/X55Uj
👉Paper arxiv.org/pdf/2504.07961
👉Project geo4d.github.io/
👉Code github.com/jzr99/Geo4D
🥊 Pose in Combat Sports 🥊
👉The novel SOTA framework for an accurate physics-based #3D human pose estimation in combat sports w/ sparse multi-cameras setup. Dataset to be released soon💙
👉Review https://t.ly/EfcGL
👉Paper https://lnkd.in/deMMrKcA
👉Project https://lnkd.in/dkMS_UrH
🧊BoxDreamer Object Pose🧊
👉BoxDreamer is a generalizable RGB-based approach for #3D object pose estimation in the wild, specifically designed to address challenges in sparse-view settings. Code coming, demo released💙
👉Review https://t.ly/e-vX9
👉Paper arxiv.org/pdf/2504.07955
👉Project https://lnkd.in/djz8jqn9
👉Repo https://lnkd.in/dfuEawSA
🤗Demo https://lnkd.in/dVYaWGcS
💛 Unified Scalable SVG Generator 💛
👉OmniSVG is the first family of e2e multimodal generators that leverages pre-trained VLMs to create detailed SVGs. Code, models & dataset to be released under MIT💙
👉Review https://t.ly/JcR3I
👉Paper https://arxiv.org/pdf/2504.06263
👉Project https://omnisvg.github.io/
👉Repo github.com/OmniSVG/OmniSVG
👉Dataset https://huggingface.co/OmniSVG
🐈 TTT Long Video Generation🐈
👉A novel architecture for video generation adapting the CogVideoX 5B model by incorporating Test-Time Training layers. Adding TTT layers into a pre-trained Transformer -> one-minute clip from text storyboards. Videos, code & annotations released💙
👉Review https://t.ly/mhlTN
👉Paper arxiv.org/pdf/2504.05298
👉Project test-time-training.github.io/video-dit/
👉Repo github.com/test-time-training/ttt-video-dit
⛽ VoRA: Vision as LoRA ⛽
👉#ByteDance unveils Vision as LoRA (VoRA), a novel paradigm converting LLMs into Multimodal Large Language Models (MLLMs) by integrating vision-specific LoRA layers. All training data, codes, and model weights available💙
👉Review https://t.ly/guNVN
👉Paper arxiv.org/pdf/2503.20680
👉Repo github.com/Hon-Wong/VoRA
👉Project georgeluimmortal.github.io/vora-homepage.github.io/
🌳 Compose Anything is out 🌳
👉Skywork AI unveils SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized videos based on textual prompts. Code, models, & evaluation benchmark released💙
👉Review https://t.ly/MEjzL
👉Paper https://arxiv.org/pdf/2504.02436
👉Project skyworkai.github.io/skyreels-a2.github.io/
👉Repo github.com/SkyworkAI/SkyReels-A2
🤗Models https://huggingface.co/Skywork/SkyReels-A2
