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 17 260 名订阅者，在 技术与应用 类别中位列第 7 543，并在 马来西亚 地区排名第 2 150 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 17 260 名订阅者。
根据 28 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 229，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 17.18%。内容发布后 24 小时内通常能获得 7.02% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 965 次浏览，首日通常累积 1 211 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 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 29 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。
17 260
订阅者
+124 小时
+17 天
+22930 天
帖子存档
🔥 Matterport 3D Semantics Dataset 🔥
👉#Meta opens HM3DSEM, the largest #3D real-world dataset with dense semantic
😎Review https://bit.ly/3yF4W4G
😎Paper arxiv.org/pdf/2210.05633.pdf
😎Project aihabitat.org/datasets/hm3d-semantics
😎Data github.com/matterport/habitat-matterport-3dresearch
🥬 "Perception Test" by #DeepMind 🥬
👉Huge dataset with obj & point tracks, temporal sounds, multiple & grounded vQA
😎Review https://bit.ly/3Vqh96Q
😎Dataset github.com/deepmind/perception_test
😎Project www.deepmind.com/blog/measuring-perception-in-ai-models
🏅GENIE by #Nvidia -> Faster Generation🏅
👉Higher-Order Denoising Diffusion Solvers for faster and better synthesis
😎Review https://bit.ly/3CRjtwr
😎Project nv-tlabs.github.io/GENIE/
😎Paper arxiv.org/pdf/2210.05475.pdf
😎Code github.com/nv-tlabs/GENIE
🍏 f-DM: Diffusion Models by Apple 🍏
👉Spectacular work by #Apple on DMs: HQ generation with better efficiency and semantic
😎Review https://bit.ly/3Tils2u
😎Project https://jiataogu.me/fdm/
😎Paper arxiv.org/pdf/2210.04955.pdf
🥏 EVA3D: new SOTA in #3D humans 🥏
👉EVA3D: new SOTA for unconditional NeRF-human generation from 2D only
😎Review https://bit.ly/3Th9qX7
😎Code github.com/hongfz16/EVA3D
😎Paper arxiv.org/pdf/2210.04888.pdf
😎Project hongfz16.github.io/projects/EVA3D.html
🔥SIMPLI: ligh novel-view synthesis🔥
👉Lightweight novel-view synthesis by #Samsung for arbitrary forward-facing scenes
😎Review https://bit.ly/3CivSYZ
😎Project samsunglabs.github.io/MLI
😎Code github.com/SamsungLabs/MLI
😎Paper samsunglabs.github.io/MLI/paper/paper.pdf
🍋 Long Video via Transformers 🍋
👉TECO is a vector-quantized latent dynamics prediction for long video
😎Review https://bit.ly/3Ch0tWD
😎Project wilson1yan.github.io/teco/
😎Paper arxiv.org/pdf/2210.02396.pdf
😎Code github.com/wilson1yan/teco
🔥 #AIwithPapers: we are 4,500+! 🔥
💙💛 Someone put the smiling 💩 under a few recent posts. But I still love you! 💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
⚛️SOTA ALERT! Particles Tracking ⚛️
👉The new SOTA in video particles tracking. "Old school" taste, with neural flavor 🧡
😎Review https://bit.ly/3CaU5Ai
😎Project particle-video-revisited.github.io/
😎Paper arxiv.org/pdf/2204.04153.pdf
😎Code github.com/aharley/pips
🔥 Human MDM: source code is out! 🔥
👉A classifier-free diffusion-based generative model for human motion domain
😎Review https://bit.ly/3rFhR2G
😎Project guytevet.github.io/mdm-page
😎Paper arxiv.org/pdf/2209.14916.pdf
😎Code github.com/GuyTevet/motion-diffusion-model
🔥 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
