现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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 17 144 名订阅者，在 技术与应用 类别中位列第 7 701，并在 马来西亚 地区排名第 2 225 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 17 144 名订阅者。
根据 25 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 -186，过去 24 小时变化为 3，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 23.94%。内容发布后 24 小时内通常能获得 6.86% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 0 次浏览，首日通常累积 1 177 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 0。
- 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 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。
17 144
订阅者
+324 小时
-367 天
-18630 天
帖子存档
🔥 Imagen Video by #Google. SICK! 🔥
👉Novel text-conditional video generation via cascade of video diffusion models 🤯
😎Review https://bit.ly/3SH2TVH
😎Project imagen.research.google/video/
😎Paper imagen.research.google/video/paper.pdf
🐢 Stable Diffusion for #Pokemon 🐢
👉Fine-tuning the stable diffusion to create a text-to-pokemon generation model
😎Review https://bit.ly/3C9qBTw
😎Tutorial https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/
🔥 VToonify: Neural Portrait Style Transfer 🔥
👉VToonify for portrait style transfer. Powered by DualStyleGAN backbone, now with #stablediffusion!
😎Review https://bit.ly/3M9wgNP
😎Demo https://t.co/8gXzF3IrpB
😎Paper arxiv.org/pdf/2209.11224.pdf
😎Project mmlab-ntu.com/project/vtoonify
😎Code github.com/williamyang1991/VToonify
🦩Phenaki: Text-to(LOOONG)Video generation🦩
👉Phenaki is an #AI capable of realistic long video synthesis, given a sequence of textual open prompts
😎Review https://bit.ly/3RwUvXx
😎Project phenaki.video/index.h
😎Paper openreview.net/pdf?id=vOEXS39nOF
🧪 Light Field Neural Rendering 🧪
👉Two-stage transformer capable of non-Lambertian effects (reflection, refraction, translucency)
😎Review https://bit.ly/3CpIFdm
😎Paper arxiv.org/pdf/2112.09687.pdf
😎Project light-field-neural-rendering.github.io
😎Code github.com/google-research/google-research/tree/master/light_field_neural_rendering
🔥DreamFusion: Text-to-3D via Diffusion🔥
👉DeepDream-like procedure to create #3D assets just from a given text
😎Review https://bit.ly/3BYY5nu
😎Paper arxiv.org/pdf/2209.14988.pdf
😎Project dreamfusion3d.github.io/gallery.html
🔥 SOTA ALERT: new Text-to-Video #AI 🔥
👉#META unveils a novel Text-to-Video (T2V) generation #AI
😎Review https://bit.ly/3E1ZDzG
😎Project https://makeavideo.studio/
😎Paper makeavideo.studio/Make-A-Video.pdf
🥇🥇 Olympic Games in 2028? 🥇🥇
👉 In a few years, the fastest runner on earth will not be a human 🥶
😎Review https://bit.ly/3Rme3O3
🔥 Semantic VISOR dataset is out! 🔥
👉Segmenting hands / active objects in egocentric video (millions masks)
😎Review https://bit.ly/3LOBLBv
😎Project epic-kitchens.github.io/VISOR/
😎Paper arxiv.org/pdf/2209.13064.pdf
🔥Diffusion Model of Neural Checkpoints🔥
👉Conditional diffusion model on Millions of checkpoints of a given task/architecture 🤯
😎Review https://bit.ly/3SBR4Qb
😎Project www.wpeebles.com/Gpt
😎Code github.com/wpeebles/G.pt
😎Paper arxiv.org/pdf/2209.12892.pdf
🔥🔥 IDE-3D: source code is out! 🔥🔥
👉Novel, photorealistic, 3D-aware facial generator: source code just released!
😎Review https://bit.ly/3BNrO2C
😎Project mrtornado24.github.io/IDE-3D/
😎Code github.com/MrTornado24/IDE-3D
😎Paper arxiv.org/pdf/2205.15517.pdf
👛 #Nvidia GET3D: #3D generative #AI 👛
👉AI-based Textured 3D meshes with complex topology, rich geometry & hi-fi textures
😎Review https://bit.ly/3SgnT5h
😎Code github.com/nv-tlabs/GET3D
😎Project nv-tlabs.github.io/GET3D/
😎Paper nv-tlabs.github.io/GET3D/assets/paper.pdf
💹 Image Synthesis @160+ FPS! 💹
👉Super-fast, 3D-Aware Image Synthesis with Sparse Voxels -> up to 167 FPS!
😎Review https://bit.ly/3r3ZNij
😎Paper arxiv.org/pdf/2206.07695.pdf
😎Project katjaschwarz.github.io/voxgraf
🦠 Motion Transformer for #selfdriving 🦠
👉The 1st place solution for 2022 #waymo "motion prediction" challenge
😎Review https://bit.ly/3f8G4LD
😎Paper arxiv.org/pdf/2209.10033.pdf
😎Code github.com/sshaoshuai/MTR
🔥#Google just announced "TensorStore"🔥
👉Novel open-source C++ / #Python library for storage/manipulation of high-dim data
😎Review https://bit.ly/3DLwbha
😎Project https://bit.ly/3C4T2TR
😎Code github.com/google/tensorstore
🍜 SURF-GAN: NeRF - >StyleGAN 🍜
👉 Editable portraits by injecting the NeRF's prior into StyleGAN
😎Review https://bit.ly/3SohEw3
😎Project jgkwak95.github.io/surfgan
😎Paper arxiv.org/pdf/2207.10257.pdf
😎Code github.com/jgkwak95/SURF-GAN
🥶 Lumos by #Nvidia: Relighting Portrait 🥶
👉The new SOTA in relighting without requiring a light stage
😎Review https://bit.ly/3dCH9ej
😎Project deepimagination.cc/Lumos
😎Paper arxiv.org/pdf/2209.10510.pdf
😎Demo http://imaginaire.cc/Lumos/
🚜 NeRF-Factory: a NeRF collection 🚜
👉PyTorch-reimplemented NeRF library with 7 popular models/implementations & 7 datasets
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF: Project | Paper | Code
✅NeRF++: Paper | Code
✅DVGO: Project | Paper v1/v2 | Code
✅Plenoxels: Project | Paper | Code
✅Mip-NeRF: Project | Paper | Code
✅Mip-NeRF360: Project | Paper | Code
✅Ref-NeRF: Project | Paper | Code
More: https://bit.ly/3qUgmgC
🦪StereoVoxelNet: RT Obstacles Detection🦪
👉Novel deep neural approach to detect occupancy from stereo images directly
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Occupancy voxels via deep learning
✅RT on Jetson-TX2 (-98% CPU of SOTA)
✅Optimization via octrees / sparse conv.
✅Real-world stereo in/outdoor dataset
More: https://bit.ly/3BylAn3
🍈SegNeXt: new SOTA in Semantic Seg.🍈
👉SOTA (by large margin) on ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel tailored network architecture
✅Spatial attention via multi-scale feats
✅Encoder + conv. better than transformers
✅SOTA on several datasets (ADE20K, etc.)
More: https://bit.ly/3UrZhrH
