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 234 Technologies & Applications7 718...

📈 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 17 166 subscribers, ranking 7 718 in the Technologies & Applications category and 2 234 in the Malaysia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 166 subscribers.

According to the latest data from 20 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -169 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 22.86%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 926 views. Within the first day, a publication typically gains 0 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 26.
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 21 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.

17 166

Subscribers

No data24 hours

-357 days

-16930 days

3 926

Post views

No data24 hours

No data48 hours

22.86%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

17 157

🍎FindTrack: text-driven VOS 🍎 👉Yonsei University introduces FindTrack, a novel decoupled framework that separates text-driven target ID from mask propagation. Impressive results (even under severe occlusions), new SOTA. Source Code & models to be released💙 👉Review https://t.ly/2smaF 👉Paper arxiv.org/pdf/2503.03492 👉Repo github.com/suhwan-cho/FindTrack

17 157

🔥Distill-Any-Depth: SOTA MDE🔥 👉Distill-Any-Depth is the new SOTA monocular depth estimation model trained with a novel knowledge distillation. Authors: ZJUT, WestLake University, LZU & NTU. Source Code, pre-trained models & HF-demo released💙 👉Review https://t.ly/GBJgi 👉Paper arxiv.org/pdf/2502.19204 👉Repo https://lnkd.in/dPtxNrQh 🤗Demo https://lnkd.in/d2TMPf4b

17 157

🔥🔥Distill-Any-Depth: new SOTA MDE🔥🔥 👉Distill-Any-Depth is the new SOTA monocular depth estimation model trained with a novel knowledge distillation. Source Code, pre-trained models & f-demo released💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Authors: ZJUT, WestLake University, LZU & NTU ✅Multiple D-normalization on pseudo-label distillation ✅Proposing novel Cross-Context Distillation approach ✅Introducing new multi-teacher distillation framework ✅Pre-trained Models and code released under MIT hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse hashtag#LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2502.19204 👉Repo https://lnkd.in/dPtxNrQh 🤗Demo https://lnkd.in/d2TMPf4b

17 157

🧠 Distractor-Aware SAM2 🧠 👉A novel distractor-aware memory for SAM2 and an introspection-based update strategy for VOT. Code & Dataset released💙 👉Review https://t.ly/RBRpQ 👉Paper arxiv.org/pdf/2411.17576 👉Project jovanavidenovic.github.io/dam-4-sam 👉Repo github.com/jovanavidenovic/DAM4SAM/

17 157

🏉 MITracker: Multi-View Tracking 🏉 👉ShangaiTech unveils MITracker, a novel Multi-View Integration Tracker, to efficiently integrate multi-view object features and provide stable tracking outcomes. Code & Dataset to be released💙 👉Review https://t.ly/RTNUo 👉Paper https://arxiv.org/pdf/2502.20111 👉Project https://xum007.github.io/MITracker.github.io/ 👉Repo https://github.com/XuM007/MITracker

17 157

👽Neural-Free Sparse Voxels Rasterization👽 👉#Nvidia unveils a novel efficient radiance field rendering algorithm that incorporates a rasterization process on adaptive sparse voxels without neural networks or 3D Gaussians. Code released (custom license)💙 👉Review https://t.ly/Nh_ic 👉Paper https://lnkd.in/g8k8Zs6R 👉Project https://lnkd.in/gR-bD4Wx 👉Repo https://lnkd.in/gNHX-w4t

17 157

🔥 YOLOv12 is out (new SOTA) 🔥 👉YOLOv12 is a novel attention-centric YOLO framework that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms. Source Code & Demo released💙 👉Review https://t.ly/jj1oR 👉Paper https://arxiv.org/pdf/2502.12524 👉Repo https://github.com/sunsmarterjie/yolov12 🤗 https://huggingface.co/spaces/sunsmarterjieleaf/yolov12

17 157

🌈L4P: Unified Low-Level 4D Vision🌈 👉#Nvidia L4P is a novel feedforward, general-purpose, architecture to solve low-level 4D perception tasks in a unified framework. L4P combines a ViTbased backbone with per-task heads that are lightweight and therefore do not require extensive training. One backbone - many SOTAs. Code announced 💙 👉Review https://t.ly/04DGj 👉Paper arxiv.org/pdf/2502.13078 👉Project research.nvidia.com/labs/lpr/l4p/ 👉Repo TBA

17 157

🔥Large Language DIFFUSION Model🔥 👉Renmin University introduces LLaDA, a *diffusion model* trained entirely from scratch, rivaling LLaMA3 8B in performance. Pre-trained from scratch on 2.3T tokens using 0.13M H800 GPU hours, followed by SFT on 4.5M pairs. A new paradigm is born? Repo by the end of Feb.25 💙 👉Review https://t.ly/7Cnrh 👉Paper https://lnkd.in/dCWi3byk 👉Project https://lnkd.in/dB7JRYeA 👉Repo https://lnkd.in/dAqzeCHJ

17 157

🔥 Animate Anyone 2 🔥 👉 The evolution of the first version that enables character animation w/ environment affordance. Amazing results but no code announced 🥲 👉Review https://t.ly/iNNLB 👉Paper https://arxiv.org/pdf/2502.06145 👉Project https://humanaigc.github.io/animate-anyone-2

17 157

Hi friends, what other kind of content would you like to *OCCASIONALLY* see in this group?

Anonymous voting

17 157

🪛 Make anything "Rig-Ready" 🪛 👉RigAnything is a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints, skeleton topologies, and assigning skinning weights in a template-free manner. Online demo announced💙 👉Review https://t.ly/bNwxq 👉Paper arxiv.org/pdf/2502.09615 👉Project www.liuisabella.com/RigAnything

17 157

🦶 It's all About Foot 🦶 👉 A collection of three works all about human foot: synthetic foot renders, reconstruction and surface normals. Repos & Datasets available💙 👉Review https://t.ly/GY8mL 👉Paper (last) arxiv.org/pdf/2502.06367 👉Projects www.ollieboyne.com/ 👉Repo github.com/OllieBoyne/FOUND 👉Repo github.com/OllieBoyne/SynFoot 👉Repo github.com/OllieBoyne/FOCUS (coming)

17 157

🥛HAMSTER: Hierarchical VLA Manipulation🥛 👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙 👉Review https://t.ly/2yXaY 👉Paper https://arxiv.org/pdf/2502.05485 👉Project https://hamster-robot.github.io/ 👉Repo TBA

17 157

🥛🥛HAMSTER: Hierarchical VLA Manipulation🥛🥛 👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Hier. Action Models w/ SeparaTEd Path Represent. ✅Fine-tuned VLMs -> to low-level 3D policy models ✅A fully open-sourced enabler for VLM-action models ✅Abundant OOD data for improving real-world control #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2502.05485 👉Project https://hamster-robot.github.io/ 👉Repo TBA

17 157

🔮Flow-Based Foundation GenAI🔮 👉Goku is the novel SOTA family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. Amazing results! Repo released (now, empty)💙 👉Review https://t.ly/dzi0O 👉Paper http://arxiv.org/pdf/2502.04896 👉Project saiyan-world.github.io/goku/ 👉Repo github.com/Saiyan-World/goku

17 157

💃HumanDiT Long-form Human💃 👉HumanDiT is a novel pose-guided Diffusion trained on a large and wild dataset w/ 14,000 hours of HQ video to produce HD videos with fine-grained bodies. Stunning results but no code announced🥲 👉Review https://t.ly/7rTRr 👉Paper https://arxiv.org/pdf/2502.04847 👉Project https://agnjason.github.io/HumanDiT-page/

17 157

🤖 META Human-Robot 🤖 👉#META PARTNR: novel benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. The largest benchmark of its kind: 100,000+ natural language tasks, spanning 60 houses and 5,819 unique objects. Code & Data (🤗) under MIT💙 👉Review https://t.ly/zcN0K 👉Paper arxiv.org/pdf/2411.00081 👉Repo github.com/facebookresearch/partnr-planner 🤗Data huggingface.co/datasets/ai-habitat/partnr_episodes

17 157

👗3D Dynamic Garments👗 👉UCLA introduces Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image. 👉Review https://t.ly/qciHV 👉Paper arxiv.org/pdf/2502.03449 👉Project dress-1-to-3.github.io

17 157

🔥 VideoJAM: #META's Video-Model (SOTA) 🔥 👉#META's VideoJAM: the new SOTA (by large margin) in motion coherence for video generation, much better than SORA! A strong motion prior into any video-gen model. Impressive results, no code announced🥲 👉Review https://shorturl.at/id7Bt 👉Paper https://arxiv.org/pdf/2502.02492 👉Project https://hila-chefer.github.io/videojam-paper.github.io/