现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Artificial Intelligence && Deep Learning
前往频道在 Telegram
Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:
显示更多📈 Telegram 频道 Artificial Intelligence && Deep Learning 的分析概览
频道 Artificial Intelligence && Deep Learning (@deeplearning_ai) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 58 024 名订阅者，在 技术与应用 类别中位列第 2 297，并在 印度 地区排名第 6 023 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 58 024 名订阅者。
根据 23 六月, 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 -218，过去 24 小时变化为 -10，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 8.90%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 5 163 次浏览，首日通常累积 0 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 15。
- 主题关注点： 内容集中在 github, learning, estimation, dataset, engineer 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：
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact:”
凭借高频更新（最新数据采集于 24 六月, 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。
58 024
订阅者
-1024 小时
-557 天
-21830 天
帖子存档
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
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🚀 Introducing L2CS-Net: Fine-Grained Gaze Estimation 👀✨
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#AI #DeepLearning #GazeEstimation #L2CSNet #OpenSource 🚀🚀 Introducing L2CS-Net: Fine-Grained Gaze Estimation 👀✨
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✅ Demo for real-time visualization
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🗂️ Prepare dataset
🏋️ Train (
python main.py --train)
🎥 Video Infernece (python main.py --demo)
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Pippo: High-Resolution Multi-View Humans from a Single Image
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🚀 3DGazeNet: Revolutionizing Gaze Estimation with Weak-Supervision! 🌟
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📖Read the Paper: Access Here
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
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Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
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⚽ FIFA World Cup 3D Human Pose Dataset ⚽
Discover WorldPose: a cutting-edge dataset for multi-person global pose estimation, featuring footage from the 2022 FIFA World Cup with 2.5M+ annotations!
Key Highlights:
✅ Authors: ETH, FIFA, Microsoft
✅ 150K+ frames, 2.5M+ 3D poses (SMPL format)
✅ Calibrated moving cameras & accurate trajectories
📄 Paper: arxiv.org/pdf/2501.02771
📊 Dataset: Access here
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🔥 Shanghai AI Lab Introduces RTMW: Real-Time Multi-Person Whole-Body Pose Estimation 🔥
• Blazing Speed: 940+ FPS on GPU, 50–300+ FPS on CPU
• High-Performance Models: Supports 2D/3D whole-body pose estimation
• Cutting-Edge Architecture: Built upon RTMPose with FPN & HEM
• Wide Dataset Coverage: Manually aligned keypoints across 14 open datasets
• Extensive Framework Support: ONNX, TensorRT, ncnn, and OpenVINO
• Open Source: Code and pre-trained models available
🔗 Discover More:
* Source code: Github
* Paper: Read the paper
✅ https://t.me/deeplearning_ai
🔥 Shanghai AI Lab Introduces RTMW: Real-Time Multi-Person Whole-Body Pose Estimation 🔥
• Blazing Speed: 940+ FPS on GPU, 50–300+ FPS on CPU
• High-Performance Models: Supports 2D/3D whole-body pose estimation
• Cutting-Edge Architecture: Built upon RTMPose with FPN & HEM
• Wide Dataset Coverage: Manually aligned keypoints across 14 open datasets
• Extensive Framework Support: ONNX, TensorRT, ncnn, and OpenVINO
• Open Source: Code and pre-trained models available
🔗 Discover More:
* Source code: Github
* Paper: Read the paper
✅ https://t.me/deeplearning_ai
OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
🔗 Discover More:
* Source code: Github
* Paper: Read the paper
✅ https://t.me/deeplearning_ai
