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 2 295，并在 印度 地区排名第 6 099 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 20 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 -216，过去 24 小时变化为 -1，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 8.37%。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 4 855 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 14。
- 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 21 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
-124 小时
-507 天
-21630 天
帖子存档
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Grounding DINO 1.5
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Depth Anything2: Unleashing the Power of Large-Scale Unlabeled Data
This work presents Depth Anything, a highly practical solution for robust monocular depth estimation by training on a combination of 1.5M labeled images and 62M+ unlabeled images.
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💻Source Code: Explore on GitHub💻Project Details: Discover More
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Are you struggling with invoking functions, passing arguments, or handling return values in Large Language Models (LLMs)?
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🔍 Discover the Power of Fine-Grained Gaze Estimation with L2CS-Net! 🌟
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🚀 Explore SCRFD: High-Efficiency, High-Accuracy Face Detection 🚀
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MLOps Masterclass
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India's Largest Free Webinar on LLMs especially focused on the recently released LLAMA-3 by Meta.
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🔍 Introducing UniRef++: Advanced Object Segmentation in Spatial and Temporal Domains
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Neural Bodies with Clothes: Overview
Introduction: Neural-ABC, a cutting-edge parametric model developed by the University of Science & Technology of China, innovatively represents clothed human bodies.
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✅Novel approach for modeling clothed human figures.
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💻Project Details: Discover More
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub
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🕷️🕷️ GenN2N: Generative NeRF2NeRF Translation.🕷️🕷️
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[Source Code]
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Introducing ECoDepth: The New Benchmark in Diffusive Mono-Depth
From the labs of IITD, we unveil ECoDepth - our groundbreaking SIDE model powered by a diffusion backbone and enriched with ViT embeddings. This innovation sets a new standard in single image depth estimation (SIDE), offering unprecedented accuracy and semantic understanding.
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