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 17 264 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 17 264 名订阅者。
根据 30 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 223，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 16.65%。内容发布后 24 小时内通常能获得 7.01% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 874 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 16。
- 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 31 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。
17 264
订阅者
+324 小时
+37 天
+22330 天
帖子存档
🔥🔥 Neural Segmentation on fire 🔥🔥
👉Novel methods for segmentation with mask calibration. Robustness++ in VOS.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Study: VOS robustness vs. perturbations
✅Adaptive object proxy (AOP) aggregation
✅Less errors due unstable pixel-level match
✅Code/models (should be) available soon
More: https://bit.ly/3yhIY6Q
🔥🔥 HD Dichotomous Segmentation 🔥🔥
👉 A new task to segment highly accurate objects from natural images.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅5,000+ HD images + accurate binary mask
✅IS-Net baseline in high-dim feature spaces
✅HCE: model vs. human interventions
✅Source code (should be) available soon
More: https://bit.ly/3ah2BDO
🔥YOLOv7: YOLO for segmentation🔥
👉YOLOv7: adding a lot of newer skills to the YOLO architecture family.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅YOLOv7, not a successor of YOLO family!
✅Framework for detection & segmentation
✅Applications based on #META detectron2
✅DETR & ViT detection out-of-box
✅Easy support for pipeline thought #ONNX
✅YOLOv4 + InstanceSegm. via single stage
✅The latest YOLOv6 training is supported!
✅Source code under GPL license.
More: https://bit.ly/3ysSJAp
🐪 BlazePose: Real-Time Human Tracking 🐪
👉Novel real-time #3D human landmarks from #google. Suitable for mobile.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MoCap from single RGB on mobile
✅Avatar, Fitness, #Yoga & AR/VR
✅Full body pose from monocular
✅Novel 3D ground truth acquisition
✅Additional hand landmarks
✅Fully integrated in #MediaPipe
More: https://bit.ly/3uvyiAv
🔥🔥YOLOv6 is out: PURE FIRE!🔥🔥
👉YOLOv6 is a single-stage object detection framework for industrial applications
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Efficient Decoupled Head with SIoU Loss
✅Hardware-friendly for Backbone/Neck
✅520+ FPS on T4 + TensorRT FP16
✅Released under GNU General Public v3.0
More: https://bit.ly/3OLjncK
🥶 E2V-SDE: biggest troll ever? 🥶
👉E2V-SDE paper (accepted to #CVPR2022) consists of texts copied from 10+ previously published papers 😂
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent ODEs for Irregularly-Sampled TS
✅Stochastic Adversarial Video Prediction
✅Continuous Latent Process Flows
✅More papers....
More: https://bit.ly/3bsL8Zw (AUDIO ON!)
🗺️Neural Translation Image -> Map🗺️
👉A novel method for instantaneous mapping as a translation problem
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Bird’s-eye-view (BEV) map from image
✅A restricted data-efficient transformer
✅Monotonic attention from lang.domain
✅SOTA across several datasets
More: https://bit.ly/39MQ76Z
🫀I M AVATAR: source code is out!🫀
👉Neural implicit head avatars from monocular videos
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅#3D morphing-based implicit avatar
✅Detailed Geometry/appearance
✅D-Rendering e2e learning from clips
✅Novel synthetic dataset for evaluation
More: https://bit.ly/3A2yzy9
🍔 Fully Controllable "NeRF" Faces 🍔
👉Neural control of pose/expressions from single portrait video
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF-control of the human head
✅Loss of rigidity by dynamic NeRF
✅3D full control/modelling of faces
✅No source code or models yet 😢
More: https://bit.ly/3OEjwi7
🦋Transf-Codebook HD-Face Restoration🦋
👉S-Lab unveils CodeFormer: hyper-datailed face restoration from degraded clips
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Face restoration as a code prediction
✅Discrete CB prior in small proxy space
✅Controllable transformation for LQ->HQ
✅Robustness and global coherence
✅Code and models soon available
More: https://bit.ly/3QEa9B5
🦑Big Egocentric Dataset by #Meta 🦑
👉Novel dataset to speed-up research on egocentric MR/AI
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅159 sequences, multiple sensors
✅Scenarios: cooking, exercising, etc.
✅‘Desktop Activities’ via multi-view mocap
✅Dataset available upon request
More: https://bit.ly/3QDccVW
🐠Largest Dataset for #autonomousdriving🐠
👉SHIFT: largest synthetic dataset for #selfdrivingcars. Shifts in cloud, rain, fog, time of day, vehicle & pedestrian density🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅4,800+ clips, multi-view sensor suite
✅Semantic/instance, M/stereo depth
✅2D/3D object detection, MOT
✅Optical flow, point cloud registration
✅Visual-Odo, trajectory & human pose
More: https://bit.ly/3HJBUUT
🍅Segmentation with INSANE Occlusions🍅
👉CMU unveils WALT: segmenting in severe occlusion scenarios. Performance over human.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅WALT: Watch & Learn Time-lapse
✅4K/1080p cams on streets over a year
✅Performance over human-supervised
✅Object-occluder-occluded neural layers
✅Source code under MIT license
More: https://bit.ly/3n7pvjO
🔥 #AIwithPapers: we are 2,900+! 🔥
💙💛 Cheers from "Black Metal Lady Gaga" plotted by DallE-mini 💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
✋HaGRID : Half Million Hands👋
👉Russian Sberbank opens HaGRID, enormous dataset for HGR. "Peace" label is present 🔵🟡
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅552,992 samples, 18 classes
✅HD resolution in RGB format
✅BBox, gesture, leading hands
✅Dataset/models available
More: https://bit.ly/3n2cd8r
🦕 SAVi++: Segmentation by #Google 🦕
👉Novel unsupervised object-centric #AI to predict depth signals from slot-based video representation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Segmenting complex dynamic scenes
✅Static/Moving objects on naturalistic BG
✅LiDAR-SAVi: segmenting in the wild
✅Source code and model soon available!
More: https://bit.ly/3n3hywd
🧠 Bias in #AI, explained simple 🧠
👉Asking DallE-Mini to help me to show what the BIAS in #AI is
𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐝 𝐒𝐚𝐦𝐩𝐥𝐞𝐬:
✅Best eng.->men/Caucasians
✅Best doctors->men/Caucasians
✅Top CEOs->men/Caucasians
✅Chef, kitchen->men/Caucasians
✅Rich People->only Caucasians
✅Poor People->non-Caucasians
✅Italian engineers->back in 30's
✅Chinese eng.->infrastructures
✅Italian working->local market
✅Chinese working->vegetables
✅Men workers->constructions
✅Women workers->only office
More: https://bit.ly/3b0UFqd
🏄🏻♀️Neural Super-Resolution in Movies🏄🏻♀️
👉Implicit neural representation to get arbitrary spatial resolution & FPS -> Super Resolution!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video as continuous video representation
✅Clips in arbitrary space/time resolution
✅OOD generalization in space-time
✅Source code and models available
More: https://bit.ly/3xsqccf
🐹 NOAH just open-sourced! 🐹
👉A novel approach to find the optimal design of prompt modules through NAS algos.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NOAH from Neural prOmpt seArcH
✅Parameter-efficient “prompt modules”
✅Efficient NAS-based implementation
✅Better than transfer, few-shot & domain gen.
More: https://bit.ly/3MKfVhi
👗BodyMap: Hyper-Detailed Humans👗
👉#META unveils 1st-ever dense continuous correspondence for clothed humans
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅1st-ever dense continuous corresp.
✅HQ fingers, hair, and clothes
✅Novel ViT-based architecture
✅SOTA on DensePose COCO
More: https://bit.ly/39nEPps
