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 17 261 名订阅者，在 技术与应用 类别中位列第 7 532，并在 马来西亚 地区排名第 2 128 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 17 261 名订阅者。
根据 29 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 219，过去 24 小时变化为 0，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 16.46%。内容发布后 24 小时内通常能获得 7.09% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 842 次浏览，首日通常累积 1 223 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 17。
- 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 30 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。
17 261
订阅者
无数据24 小时
+27 天
+21930 天
帖子存档
💜 #Selfdriving in 80's. Damn Romantic 💜
👉The first self-driving car with people on board, 1986. So slow and lovely.
More: https://bit.ly/3BtRDon
🥤K-VIL: Keypoint-based visual imitation🥤
👉K-VIL: auto-incremental extraction of object-centric task representation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Efficient task-relevant keypoints
✅Embodiment-independent tasks
✅Adaptation of tasks to new scenes
✅Input: only a small set of demo clips
✅Novel keypoint-based controller
More: https://bit.ly/3eIrxpP
🐲 Open-Source Self-Driving projects 🐲
👉A free repo with many autonomous vehicle-related projects
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Basic/Advance Lane/Line Detection
✅Driving behavior by training & validating
✅Autopilot: predicting steering angle
More: https://bit.ly/3qqJ7RB
🎪 SOTA in Arbitrary Shape Text Detection 🎪
👉Novel unified coarse-to-fine Transformer for arbitrary shape text detection
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Coarse-to-fine arbitrary text detection
✅Accurate text detection, NO post-process
✅Boundary proposal generation mechanism
✅Innovative boundary transformer (iterative)
✅Boundary energy loss (BEL) for refinement
More: https://bit.ly/3D6Ryt4
👹TT-GNeRF: generative NeRF for Faces👹
👉TT-GNeRF: a novel 3D-aware GANs based on generative NeRF for faces
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ETH + Uni_Trento + #Snap 🤯
✅DAEM for disentanglement of 3D model
✅"Training-as-Init, Optimizing-for-Tuning"
✅Consistency++, preserving non-target ROI
✅Unsupervised optimization of geometry
More: https://bit.ly/3ARZmMw
🌈 X-NeRF: Cross-Spectral NeRF 🌈
👉Cross-Spectral NeRF from cams with different light spectrums
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅First ever cross-spectral NeRF
✅Avoiding non-trivial calib/match
✅Normalized Cross-Device Coords
✅Novel dataset w/ RGB, MS, & IR
More: https://bit.ly/3RqHnUo
🐠VIS - Deformable Transformers 🐠
👉DeVIS: VIS method with efficiency and performance of deformable ViT
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Temp. multi-scale D-Attention
✅Instance-aware object queries
✅Mask: DA + multi-scale feats map
✅Improved multi-cue clip tracking
✅SOTA on YouTube-VIS 2021/OVIS
More: https://bit.ly/3TQv1Xc
🤯 #StableDiffusion + #Dallemini = BOOM! 🤯
👉A #colab notebook that combines Stable Diffusion + DALL-E Mini (Craiyon)
More: https://bit.ly/3TTOshR
🦎 VMT: Video Mask Transfiner 🦎
👉Novel highly efficient ViT structure for video instance segmentation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅HD & more temporally stable mask
✅Higher resolution features for VIS
✅Detecting error-prone s-t. regions
✅Auto-refinement on training data!
More: https://bit.ly/3RKXtb4
🫐 Stable Diffusion Video is out! 🫐
👉A free notebook to generate videos by interpolating the latent space of SD.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Blueberry to strawberry spaghetti
✅Dream items from same prompt
✅Morph different prompts (seeds)
✅Built on a script by A. Karpathy
More: https://bit.ly/3ey8632
🌪️ TimeLapse++: Video Temporal Pyramid🌪️
👉Multi-scale lens to view the passage of time: far beyond a "classic" timelapse
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Inspired by "old-school" spatial pyramids
✅Video Spectrogram to go through pyramid
✅Months/years of data in a few seconds!
✅Multi-temporal freq., no aliasing
More: https://bit.ly/3TKnYPS
🥑 DALL·E: Outpainting via #NLP 🥑
👉Extending any original image, creating large-scale images in any aspect ratio
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Extending an image beyond its borders
✅Visual elements in same style of the input
✅Driving the image "story" in new directions
✅Shadows, reflections & textures w/ context
More: https://bit.ly/3eoH8uD
🪨Controllable #3D Adversarial Face🪨
👉#Meta (+CMU) on decoupling identity/expression + granular control over expressions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Supervised auto-enc. + GAN
✅UV texture maps + 3D faces
✅Control expression, saving ID
✅Code under X11 License
More: https://bit.ly/3AVE80q
🚗 Massive Dataset in Virtual Cities 🚗
👉Synthehicle: 7 hours of labeled material, 340 cams, 64 days, rain, dawn, & night scenes.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Multi-target multi-cam tracking
✅2D, 3D, segm. & depth annotations
✅Instance, semantic & panoptic segm.
✅340 clips, 64 scenes, 17 hrs, 4M BBs
More: https://bit.ly/3TArHiV
🧡 Avatarization in 90's. So Romantic 🧡
👉Making of the first #MortalKombat in early 90's
More: https://bit.ly/3wTSpJB
🍊StableFace: Talking Face Generation🍊
👉Analysis on motion jittering in 3D face generation (audio-in -> video-out)
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Motion jittering analysis for stability
✅Gaussian-based adaptive smoothing
✅Augmented erosions of neural renderer
✅Audio-fused generator for dependency
More: https://bit.ly/3Kt95gI
🎍#3D scene manipulation from 2D🎍
👉Reconstruct, decompose, manipulate & render 3D scenes in a single pipeline
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unique 3D, non-occupied space from 2D
✅Inverse query algorithm for shapes
✅First synthetic dataset for 3D editing
More: https://bit.ly/3RlYhTY
🔵 Deep Saliency: driving the attention 🔵
👉Google unveils a family of operators to "drive" human saliency
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Editing image to drive Saliency
✅Transforms to hide distractors
✅Warping operator for distractor
✅GAN-op for less-saliency altern.
More: https://bit.ly/3KoQQc2
🔥 #AIwithPapers: we are 4,000+! 🔥
💙💛Lot of people joined, and we talked about #StableDiffusion only twice! Can't believe it.💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
🔥IDOL (#CVPR2022 winner): code is out!🔥
👉IDOL for VIS: outperforming all online/offline methods, the new SOTA!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Online usually inferior by >10AP
✅Online based on contrast-learning
✅Discriminative++ instance embeddings
✅Full exploiting history for stability
More https://bit.ly/3dXCDXw
