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AI with Papers - Artificial Intelligence & Deep Learning
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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 17 242 subscribers, ranking 7 697 in the Technologies & Applications category and 2 240 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 242 subscribers.
According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -95 over the last 30 days and by 6 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 20.69%. Within the first 24 hours after publication, content typically collects 7.58% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 568 views. Within the first day, a publication typically gains 1 307 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
- 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 06 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.
17 242
Subscribers
+624 hours
-627 days
-9530 days
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| Date | Subscriber Growth | Mentions | Channels | |
| 06 June | 0 | |||
| 05 June | +6 | |||
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| 01 June | +2 |
Channel Posts
🔍 Nvidia Locate Anything 🔍
👉Diverse localization tasks under a unified vision-language model, including document understanding, GUI grounding, dense detection, and OCR. Repo released💙
👉Review https://t.ly/PvwFo
👉Paper https://lnkd.in/dWfNpzPZ
👉Project https://lnkd.in/dM89BX-8
👉Repo https://lnkd.in/dC4KCQSM
| 2 | 🪔Latent Decoding with Pixel Diffusion🪔
👉PiD by Nvidia is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass. Repo under Apache 2.0💙
👉Review https://t.ly/y19mA
👉Paper https://lnkd.in/duVC25C2
👉Project https://lnkd.in/dW6TkzCB
👉Repo https://lnkd.in/dnGdgKRr | 3 568 |
| 3 | 🍒Count Anything, Any Granularity🍒
👉Open-world counting as multi-grained counting, where visual exemplars specify target appearance and fine-grained text specifies the intended semantic granularity across five explicit levels. Repo/Data under Apache💙
👉Review https://t.ly/nqz80
👉Paper https://lnkd.in/dp7khTRU
👉Project https://lnkd.in/d_jfX_Yn
👉Repo https://lnkd.in/dkTRGZkG
👉Data https://lnkd.in/dB83jRyT | 5 170 |
| 4 | 🦄Unified Correspondence Transformer🦄
👉UniCorrn is the first correspondence model with shared weights that unifies 2D-2D, 2D-3D, and 3D-3D geometric matching with an end-to-end transformer architecture. Repo under CC BY-NC-SA 4.0💙
👉Review https://t.ly/2OBdq
👉Paper https://arxiv.org/pdf/2605.04044
👉Project https://neu-vi.github.io/UniCorrn/
👉Repo https://github.com/neu-vi/UniCorrn | 5 360 |
| 5 | About the frequency of posting in the channel: | 4 485 |
| 6 |  🪝Syn4D: Multiview Synthetic 4D Dataset🪝
👉Syn4D is novel multi-view synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations💙
👉Review https://t.ly/SL1mk
👉Paper https://arxiv.org/pdf/2605.05207
👉Project https://jzr99.github.io/Syn4D/
👉Repo https://github.com/jzr99/Syn4D
👉Data huggingface.co/datasets/Syn4D/Syn4D_RGBD/tree/main | 3 999 |
| 7 | 🧘♀️Holistic Shot Boundary Detection🧘♀️
👉OmniShotCut detects shot changes of the video in diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.), and recognize Sudden Jump and Transitions (dissolve, fade, wipe, etc.) by proposing a Shot-Query-based Video Transformer. Repo, demo & benchmark💙
👉Review https://t.ly/sTi7N
👉Paper https://arxiv.org/pdf/2604.24762
👉Project uva-computer-vision-lab.github.io/OmniShotCut_website/
👉Repo github.com/UVA-Computer-Vision-Lab/OmniShotCut | 4 295 |
| 8 | 🛒 Reshoot-Anything is out 🛒
👉Reshoot-Anything reshoots dynamic monocular videos under novel camera trajectories. Code under Apache 2.0 💙
👉Review https://t.ly/MIqAc
👉Paper https://arxiv.org/pdf/2604.21776
👉Project adithyaiyer1999.github.io/reshoot-anything/
👉Repo github.com/morphicfilms/video-to-video | 0 |
| 9 | 💙 PY4AI 2026: here we are! 💙
👉The third edition of our conference is official! Speaker list and (free) tickets: https://t.ly/L4_52 | 0 |
| 10 | 🎈Face Anything 4D (SOTA)🎈
👉A novel unified 4D facial reconstruction and dense tracking from image sequences: new SOTA in facial single-image and mono-video depth estimation, dense 4D reconstruction, and 3D point tracking. Repo & Dataset announced💙
👉Review https://t.ly/zItie
👉Paper https://arxiv.org/pdf/2604.19702
👉Project kocasariumut.github.io/FaceAnything
👉Repo TBA | 0 |
| 11 | 🌗Mobile Ultra-detailed Avatars🌗
👉Given skeletal poses and a virtual camera as inputs, MUA by Max Planck Institute produces photorealistic renderings and hyper-detailed geometry of animatable clothed humans. Repo announced💙
👉Review https://t.ly/QPCy6
👉Paper https://arxiv.org/pdf/2604.18583
👉Project https://vcai.mpi-inf.mpg.de/projects/MUA/
👉Repo TBA | 0 |
| 12 | 👩🦰 3D Head w/ Deformable Hair 👩🦰
👉Xi’an Jiaotong University unveils a novel method that reconstructs decoupled 3D Gaussian head avatars from a single input image: effortless hairstyle transfer with natural dynamic hair motion. Code announced💙
👉Review https://t.ly/kWZdd
👉Paper https://arxiv.org/pdf/2604.14782
👉Project yuansun-xjtu.github.io/CompHairHead.io/
👉Repo yuansun-xjtu.github.io/CompHairHead.io/ | 0 |
| 13 |  🐞GCT 3D Reconstruction🐞
👉ANT unveils LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. Repo under A-NC 4.0 International💙
👉Review https://t.ly/ExodA
👉Paper https://arxiv.org/pdf/2604.14141
👉Project https://arxiv.org/pdf/2604.14141
👉Repo github.com/robbyant/lingbot-map | 0 |
| 14 | 📱3D Human-Object Contact📱
👉Pi-HOC by CMU + NREC is a novel single-pass, instance-aware framework for dense 3D semantic contact prediction of all human-object pairs. Repo announced💙
👉Review https://t.ly/TAgG1
👉Paper https://arxiv.org/pdf/2604.12923
👉Project https://pi-hoc.github.io/
👉Repo https://github.com/SravanChittupalli/Pi-HOC | 0 |
| 15 | 🐓Interactive Objects from EgoVideo🐓
👉EgoFun3D by Simon Fraser University is a coordinated task, dataset and benchmark for modeling interactive 3D objects from egocentric videos. Repo (TBA), demo & dataset💙
👉Review https://t.ly/YhGN7
👉Paper arxiv.org/pdf/2604.11038
👉Project 3dlg-hcvc.github.io/EgoFun3D/
👉Repo github.com/3dlg-hcvc/EgoFun3D
👉Demo bc79fea884062374b3.gradio.live/ | 0 |
| 16 | 🧴OmniShow: Automatic Contents Creation🧴
👉OmniShow is the novel SOTA in content creation with industry-grade performance. Impressive results, best with audio. Repo announced💙
👉Review https://t.ly/Pm-7U
👉Paper arxiv.org/pdf/2604.11804
👉Project correr-zhou.github.io/OmniShow/
👉Repo github.com/Correr-Zhou/OmniShow | 0 |
| 17 | 🔥SOTA 3D Detection in the wild🔥
👉WildDet3D is a novel unified geometry-aware architecture that natively accepts text, point, and box prompts and can incorporate auxiliary depth signals at inference time. New SOTA! Repo, models & #iphone💙
👉Review https://t.ly/8NxBN
👉Paper https://arxiv.org/pdf/2604.08626
👉Project https://allenai.github.io/WildDet3D/
👉Repo https://github.com/allenai/WildDet3D | 0 |
| 18 | 🐞6D Object Pose w/ Deformation🐞
👉DeSOPE by Xidian & #MagicLeap is a novel large-scale dataset for 6DoF deformed objects: 665K pose annotations produced via a semiautomatic pipeline. Repo & Dataset announced💙
👉Review https://t.ly/M5VgX
👉Paper https://arxiv.org/pdf/2604.06720
👉Project https://desope-6d.github.io/
👉Repo TBA | 0 |
| 19 | 🪞1.1M Metric VTON Dataset🪞
👉Google's Fit-Inclusive Try-on: large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. Repo & dataset announced💙
👉Review https://t.ly/cs-pt
👉Paper arxiv.org/pdf/2604.08526
👉Project johannakarras.github.io/FIT/
👉Repo TBA | 0 |
| 20 |  Here the preview, tomorrow the full clip from official source :) | 0 |
