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

Malaysia2 225 Technologies & Applications7 701...

📈 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 17 144 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 17 144 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.

17 144

Subscribers

+324 hours

-367 days

-18630 days

4 105

Post views

~ 1 17724 hours

~ 1 35348 hours

23.94%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

17 138

🐍 Implicitron: "democratizing" NeRF🐍 👉#META opens a novel framework for NeRF-world in #PyTorch3D #pytorch 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Implicit representations (NeRF) / Render ✅RaySampler/PointSampler & more ✅NeRF’s MLP, IDR’s FF, SRN, etc. ✅Renderers: MEAR, LSTMRenderer, etc. More: https://bit.ly/3bPyJPJ

17 138

🔥Stable Diffusion on clips. INSANE🔥 👉The most advanced latent text-to-image DM. #RunwayML just announced is going to apply it on clips 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Latent DM on 512p from LAION-5B ✅Frozen CLIP ViT-L/14 text encoder ✅Lightweight, runs on a 10GB-GPU ✅Checkpoints only for research More: https://bit.ly/3QfkRx3

17 138

🔥🔥UPDATE🔥🔥 Code Released: https://github.com/google-research/jax3d/tree/main/jax3d/projects/mobilenerf

17 138

🍨 Scaling Neural Indoor Scene 🍨 👉Neural scene rendering for indoor: scalable in both training/rendering 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Neural scene rendering for indoor ✅#3D into tiles with MLPs to scale up ✅Parallel training of tile-based MLPs ✅View-indep. components (via surf-MLP) More: https://bit.ly/3bH94IX

17 138

🎰 Texturify: Neural Textures Generator 🎰 👉A step towards automated content creation. HQ textures directly on surface of 3D object 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅TUM + Max Planck + Apple 🍏 ✅Realistic, HQ textures from 2D pics ✅3D shape geometry, no 3D supervision ✅3D-aware surface-based generation net More: https://bit.ly/3BW7UUU

17 138

🪰 EasyMocap: Open Neural Mocap 🪰 👉EasyMocap: open-source marker-less mocap with novel view synthesis from RGB 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 (of last paper added): ✅Editable free-viewpoint video ✅Layered neural representation of humans ✅Multi-pax -> instances, weakly-supervised ✅HQ neural representation of the humans ✅Addressing camera error by human poses More: https://bit.ly/3p6lUDO

17 138

🥇#NVIDIA wins SIGGRAPH's Best Paper🥇 👉Instant #NeRF awarded as a best paper at SIGGRAPH 2022! 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Speed-up of several orders of magnitude ✅HQ neural primitives in a matter of secs ✅Render in tens of milliseconds at 1080p ✅Source code and resources available! More: https://bit.ly/3Qt8c9D

17 138

🧊EPro-PnP: Persp-n-Points Detection🧊 👉EPro-PnP: probabilistic PnP layer for general e2e pose estimation 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Probabilistic PnP for general e2e pose ✅Top-tier in 6DoF by inserting into CDPN ✅Deformable accurate detection ✅2D-3D corresp. learned from scratch More: https://bit.ly/3BNPXYr

17 138

🎹🎹 Learning Piano in #AR 🎹🎹 👉PianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Sheet Insight to learn sight-read ✅MIDI keyboard connectivity ✅Air piano for no physical pianos ✅Multiplayer Music Instruction ✅PianoVision Music Hall in #VR More: https://bit.ly/3zYvwGX

17 138

🍑 World-Object Detection via ViT 🍑 👉Google unveils OWL-ViT: open-vocabulary detector based on ViTs 🤯 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅ViTs for Open-World Localization ✅Img-level to open-vocabulary detection ✅SOTA one-shot (img.cond.) detection More: https://bit.ly/3Sy3jOj

17 138

🔥PCVOS: clip-wise mask VOS🔥 👉PCVOS: new semi-supervised video object segmentation method 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Reformulating semi-supervised VOS ✅Novel per-clip inference perspective ✅Clip-wise operation on intra-clip ✅PCVOS: model for per-clip inference ✅New SOTA on multiple benchmarks More: https://bit.ly/3vJtmbz

17 138

☀️LocoProp: Neural Layers Composition☀️ 👉Google AI unveils LocoProp: novel neural paradigm for modular composition of layers. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Backprop++ via Local Loss Optimization ✅Layer-based w-reg, target output, loss ✅Multiple local update via first-order opt. ✅Superior performance and efficiency More: https://bit.ly/3Q40YJn

17 138

🔥🔥MultiNeRF: three NeRFs are out!🔥🔥 👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Paper_1: Mip-NeRF 360 ✅Paper_2: Ref-NeRF ✅Paper_3: NeRF in the Dark More: https://bit.ly/3QjpRRc

17 138

🔥 MinVIS, a new SOTA is out 🔥 👉#Nvidia miniVIS: no video-based architectures nor training procedures🤯 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Video architecture/train not required ✅MinVIS outperforms the previous SOTA ✅Occluded VIS (OVIS): >10% improvement ✅1% of labeled frames >> fully-supervised More: https://bit.ly/3pcYzk1

17 138

🚀 #VR by NASA - 1985 🚀 👉Q: is #VR the technology that developed least in the last 40 years? 🤔 Let's talk: https://bit.ly/3JxDZ7i

17 138

👩‍🦰 Real-Time Neural Hair 👩‍🦰 👉Accurate hair geometry & appearance from multi-pics 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Bonn, CMU and Reality Labs ✅Photorealistic Real-Time render ✅HQ strand geometry/appearance ✅Novel scalp texture description ✅Intuitive manipulation of 3D hair More: https://bit.ly/3vBiH2G

17 138

🧣NeRF for Outdoor Scene Relighting🧣 👉NeRF-OSR: the first neural radiance fields approach for outdoor scene relighting 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅NeRF-method for outdoor relighting ✅Simultaneous illumination/viewpoint ✅Control over shading, shadow, albedo ✅Self-Supervised training from outdoor ✅Dataset: 3240 viewpoints, 110+ times More: https://bit.ly/3vBiH2G

17 138

🔥🔥UPDATE🔥🔥 Code Released: https://github.com/andreas128/RePaint

17 138

🔥 MobileNeRF is out -> Pure Fire! 🔥 👉MobileNeRF is out: the mobile evolution of NeRF via textured polygons. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Same quality, 10x faster than SNeRG ✅Memory-- by storing surface textures ✅Integrated GPUs: less memory/power ✅Suitable for browser & viewer is HTML More: https://bit.ly/3PUKPWy

17 138

🔥AND/OR: Composable Diffusion Models🔥 👉Novel neural compositional generation via Composable Diffusion Models 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅DM as energy-based models ✅Connecting diffusion models ✅Conjunction & negation, on top of DM ✅Zero-shot combinatorial generalization More: https://bit.ly/3PYv1Cs