AI with Papers - Artificial Intelligence & Deep Learning
前往频道在 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
显示更多📈 Telegram 频道 AI with Papers - Artificial Intelligence & Deep Learning 的分析概览
频道 AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 17 259 名订阅者，在 技术与应用 类别中位列第 7 509，并在 马来西亚 地区排名第 2 130 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 17 259 名订阅者。
根据 25 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 261，过去 24 小时变化为 2，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 16.91%。内容发布后 24 小时内通常能获得 7.04% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 918 次浏览，首日通常累积 1 216 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 16。
- 主题关注点： 内容集中在 framework, object, dataset, tba, depth 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“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”
凭借高频更新（最新数据采集于 26 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
17 259
订阅者
+224 小时
+17 天
+26130 天
帖子存档
🦖T-Rex: Counting by Visual Prompting🦖
👉T-Rex: a novel interactive object counting model to detect and count any objects. Impressive results!
👉Review https://t.ly/4SfFX
👉Project https://lnkd.in/dVtEndHv
👉Paper https://lnkd.in/dBGQsbdP
👉Code (not announced, but an empty repo exists): https://lnkd.in/dnZnGRUn
🧿 Model-aware 3D Eye Gaze 🧿
👉 Novel hybrid approach that outputs 3D eye model, semantic segmentation, cam-intrinsic & pose. Only 2D eye semantic segmentation masks and fewer 3D gaze labels for supervision.
👉Review https://t.ly/AdKRf
👉Paper https://lnkd.in/dWb9GHPh
👉Code https://lnkd.in/dfAWFVky
🔳 SOTA Semantic Boundary 🔳
👉Mobile-Seed, a lightweight, dual-task framework tailored for simultaneous semantic segmentation and boundary detection.
👉Review https://t.ly/GsArZ
👉Project whu-usi3dv.github.io/Mobile-Seed/
👉Paper arxiv.org/pdf/2311.12651.pdf
👉Code github.com/WHU-USI3DV/Mobile-Seed
🍿 Segmenting anything in 3D 🍿
👉 OmniSeg3D: omniversal segmentation method aims for segmenting anything in 3D all at once.
👉Review https://t.ly/Q0jrK
👉Paper https://lnkd.in/d9qpxXY9
👉Code (soon)
🌦️ 100+ GPU weather training 🌦️
👉#NVIDIA just released Makani: massively parallel training of weather and climate prediction models on 100+ GPUs and to enable the development of the next generation of weather and climate models.
👉 Discussion https://lnkd.in/dMgakzWm
👉 Project & Code https://lnkd.in/d4NFZ5xi
🐓 Emu: image edit / video gen. 🐓
👉#Meta the new SOTA in text-to-video generation and instruction-based image editing.
👉 Review https://t.ly/PMTBc
👉 Paper (image edit): https://lnkd.in/eVadH-QS
👉 Project https://lnkd.in/eG8eWUJY
👉 Paper (video gen): https://lnkd.in/eVadH-QS
👉 Project https://lnkd.in/eu6Zu6gp
💥🚗 CrashCar101: Generative Damaged Cars💥🚗
👉 CrashCar101: procedural generation pipeline that damages 3D car models to obtain synthetic damaged cars paired with pixel-accurate annotations
👉 Review https://t.ly/pITHm
👉 Paper https://lnkd.in/dzp6q3T5
👉 Project https://lnkd.in/daRXg73N
🔥Florence-2: unified Computer Vision🔥
👉#Microsoft announces Florence-2: novel foundation model with unified, prompt-based, representation for a large variety of #computervision & vision-language task. One backbone -> multiple tasks!
👉Review https://t.ly/pOins
👉Paper arxiv.org/pdf/2311.06242.pdf
👉Project www.microsoft.com/en-us/research/project/projectflorence/
🪐 Spacecraft Pose Estimation 🪐
👉SnT (Luxembourg) unveils the most advanced event-based dataset for Spacecrafts: Unreal Engine + data from ICNS simulator + Real images + Real event data acquired in lab
😎Review https://t.ly/m8JPB
😎Paper https://lnkd.in/d_edvc3n
😎Project https://lnkd.in/dPp375aY
🫀 Neural Segmentation of Human 🫀
👉TotalSegmentator_v2: segmenting 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) in CT. Now suitable in 3D Slicer, open source platform for image visualization.
😎Review https://t.ly/yHMm1
😎Code https://lnkd.in/dvgrbsCE
😎Paper https://lnkd.in/dkwHuuzU
🔥 Hu.ma.ne #AI Pin is out! 🔥
👉Hu.ma.ne just launched #AI Pin: the new standalone AI-powered screenless device. Running on the GPT-4 LLMs, suitable for real-time translation. #AI-powered camera and laser projector
😎 More https://t.ly/CXz95
🐪 30x Faster Neural Large Scenes 🐪
👉 NeuRas: realistic real-time novel-view synthesis of VERY large scenes (>10000 m2 ). 30× faster rendering than previous SOTA w/ comparable or better realism
😎Review https://t.ly/ELJSE
😎Paper https://arxiv.org/pdf/2311.05607.pdf
😎Project https://waabi.ai/NeuRas/
🛋️ 3DiffTection: new SOTA 3D detection 🛋️
👉#Nvidia unveils 3DiffTection, the new SOTA for 3D object detection from single images. A powerful 3D detector powered by diffusion model
😎Review https://t.ly/PciXY
😎Project research.nvidia.com/labs/toronto-ai/3difftection
😎Paper https://arxiv.org/pdf/2311.04391.pdf
😎Code https://github.com/nv-tlabs/3DiffTection
🥻Sewformer: Towards Virtual Cloth🥻
👉SEA AI Lab unveils a novel #AI to recovery the garment sewing patterns from daily photos for #AR / #VR worlds
😎Review https://t.ly/MwpAV
😎Project https://sewformer.github.io/
😎Paper https://arxiv.org/pdf/2311.04218.pdf
😎Code https://github.com/sail-sg/sewformer
🔥 Does GPT-4 Pass the Turing Test? 🔥
👉No. I mean...not yet. Read this Paper from UC San Diego👇
😎Review https://t.ly/o8HgM
😎 Paper https://arxiv.org/pdf/2310.20216.pdf
🚛 OYSTER: unsupervised detection w/ LIDAR 🚛
👉Waabi unveils OYSTER: a novel unsupervised object detection from LiDAR point clouds.
😎Review https://t.ly/EMi58
😎Project https://waabi.ai/oyster/
😎Paper arxiv.org/pdf/2311.02007.pdf
👣 Foot via Synthetic Data 👣
👉 50,000 synthetic/photorealistic foot images + a novel SOTA library for foot
😎Review https://t.ly/TVanP
😎Paper https://arxiv.org/pdf/2310.18279.pdf
😎Project https://ollieboyne.github.io/FOUND
😎Code https://github.com/OllieBoyne/FOUND
🍄 Video Understanding with GPT-4V(ision) 🍄
👉 #Microsoft unveils MM-Vid, the most advanced video understanding framework (w/ #chatgpt4). Impressive results on long-form videos and intricate tasks such as audio description & multimodal high-level comprehension
😎Review https://t.ly/RISMm
😎Paper arxiv.org/pdf/2310.19773.pdf
😎Project https://multimodal-vid.github.io
✌️ Relighted 3D Interacting Hands 🤞
👉#META unveils Re:InterHand: a large dataset of relighted 3D interacting hands
😎Review https://t.ly/I1dQk
😎Paper arxiv.org/pdf/2310.17768.pdf
😎Project mks0601.github.io/ReInterHand
😎Data github.com/mks0601/ReInterHand
🧂 SOTA RGB-D Video Salient Object 🧂
👉 DCTNet+ (model) and RDVS(dataset) for a new SOTA in Video Saliency Object Detection
😎Review https://t.ly/DapLV
😎Code github.com/kerenfu/RDVS
😎Paper arxiv.org/pdf/2310.15482.pdf
