اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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
نمایش بیشتر📈 تحلیل کانال تلگرام AI with Papers - Artificial Intelligence & Deep Learning
کانال AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 17 166 مشترک است و جایگاه 7 718 را در دسته فناوری و برنامهها و رتبه 2 234 را در منطقه ماليزيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 166 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 20 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -169 و در ۲۴ ساعت گذشته برابر 0 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 22.86% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً N/A% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 3 926 بازدید دریافت میکند. در اولین روز معمولاً 0 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 26 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند framework, object, dataset, tba, depth تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 21 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
17 166
مشترکین
اطلاعاتی وجود ندارد24 ساعت
-357 روز
-16930 روز
آرشیو پست ها
🍎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/
