Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
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
Ko'proq ko'rsatish📈 Telegram kanali AI with Papers - Artificial Intelligence & Deep Learning analitikasi
AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 17 166 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 718-o'rinni va Malayziya mintaqasida 2 234-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 166 obunachiga ega bo‘ldi.
20 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -169 ga, so‘nggi 24 soatda esa 0 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 22.86% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 926 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent framework, object, dataset, tba, depth kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 21 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
17 166
Obunachilar
Ma'lumot yo'q24 soatlar
-357 kunlar
-16930 kunlar
Postlar arxiv
🍎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
🔥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
🔥🔥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
🧠 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/
🏉 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
👽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
🔥 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
🌈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
🔥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
🔥 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
Hi friends, what other kind of content would you like to *OCCASIONALLY* see in this group?
🪛 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
🦶 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)
🥛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
🥛🥛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
🔮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
💃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/
🤖 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
👗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
🔥 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/
