Available now! Telegram Research 2025 — the year's key insights 
AI with Papers - Artificial Intelligence & Deep Learning
Open in 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
Show more📈 Analytical overview of Telegram channel AI with Papers - Artificial Intelligence & Deep Learning
Channel AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) in the English language segment is an active participant. Currently, the community unites 17 136 subscribers, ranking 7 701 in the Technologies & Applications category and 2 225 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 136 subscribers.
According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -186 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 23.94%. Within the first 24 hours after publication, content typically collects 6.86% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 1 177 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as framework, object, dataset, tba, depth.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 26 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
17 136
Subscribers
+324 hours
-367 days
-18630 days
Posts Archive
🔥OmniBenchmark: CV beyond ImageNet🔥
👉 21 realms, 7,000+ concepts and 1M+ images. Far beyond ImageNet!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅vs. ImageNet: 2.5x realms, 9x concepts
✅Conciseness: no concept overlapping
✅ReCo: Relational Contrastive Learning
✅New supervised contrastive learning SOTA
More: https://bit.ly/3RJRKU0
🔥Grand Unification of Object Tracking🔥
👉UNICORN: unified method for SOT, MOT, VOS, & MOTS with a single neural net. 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Great unification for 4 tracking tasks
✅Bridging methods / pixel-wise corresp.
✅SOTA on 8 challenging benchmarks
✅Source code under MIT License
More: https://bit.ly/3o74h6g
🔥🔥 Update 🔥🔥
👉Code https://github.com/THUDM/CogVideo
👉Demo https://wudao.aminer.cn/cogvideo/
More: https://bit.ly/3yP86BQ
🍰 Long-Term Object Segmentation 🍰
👉XMem: object segmentation for long clips with unified feature memory stores
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Inspired by Atkinson–Shiffrin model
✅Stores with different temporal scales
✅Memory consolidation algorithm
✅Compact/powerful long-term memory
✅Source code and models available
More: https://bit.ly/3PP0EOn
☀️ 4D Neural Relightable Humans ☀️
👉Relighting4D: free-viewpoints relighting of humans under unknown illuminations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Relight dynamic, free viewpoints
✅Disentangled reflectance/geometry
✅SOTA on synthetic/real datasets
✅Code/models under MIT License
More: https://bit.ly/3RF3yH9
🤹♂️ K-Means Mask Transformer 🤹♂️
👉#Google AI unveils kMaX-DeepLab, novel E2E method for segmentation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅kMaX-DeepLab: k-means Mask Xformer
✅Rethinking relationship pixels / object
✅Cross-attention -> k-means clustering
✅The new SOTA on several dataset
More: https://bit.ly/3O2QV5I
👽 Neural I2I with a few shoots 👽
👉#Alibaba unveils a novel portrait stylization. Limited samples (∼100) -> HD outputs
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Calibration first, translation later
✅Balanced distribution to calibrate bias
✅Spatially semantic constraints via geometry
✅Source code and models soon available!
More: https://bit.ly/3IwOmHO
📟📟AI-Designed Circuits with Deep RL📟📟
👉#Nvidia unveils an #AI to design circuits from scratch, smaller and faster than SOTA ones
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Parallel prefix circuits for Hi-Perf
✅RL framework to explore the circuit space
✅Smaller, Faster, Power-- from the scratch
More: https://bit.ly/3yY9dk7
🦒 Text2LIVE: Text-Driven Neural Editing 🦒
👉#Amazon unveils a novel #AI for text-driven edit of videos. Insane! 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Semantic edits of real-world videos
✅Edit layer–RGBA representing target
✅Edit layers synthesized on single input
✅No masks or a pre-trained generator
More: https://bit.ly/3NVP6aE
😊😎 Seq-DeepFake via Transformers 😎😊
👉S-Lab opens Seq-DeepFake: Detecting Sequential DeepFake Manipulation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Seq-DeepFake: sequences of facial edits
✅Dataset: 85k #deepfake manipulation
✅Powerful Seq-DeepFake Transformer
✅Code, dataset and models available!
More: https://bit.ly/3ACQXhi
🔥🔥 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
