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 17 151 مشترک است و جایگاه 7 726 را در دسته فناوری و برنامهها و رتبه 2 240 را در منطقه ماليزيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 151 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 21 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -166 و در ۲۴ ساعت گذشته برابر -6 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 23.63% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 6.86% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 4 057 بازدید دریافت میکند. در اولین روز معمولاً 1 177 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 22 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.
17 151
مشترکین
-624 ساعت
-277 روز
-16630 روز
آرشیو پست ها
🌹 Physics-Based 3D Video-Gen 🌹
👉PhysDreamer, a physics-based approach that leverages the object dynamics priors learned by video generation models. It enables realistic 3D interaction with objects
👉Review https://shorturl.at/bivP4
👉Paper https://arxiv.org/pdf/2404.13026.pdf
👉Project https://physdreamer.github.io/
👉Code github.com/a1600012888/PhysDreamer
🛞 6Img-to-3D driving scenarios 🛞
👉EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics
👉Review https://shorturl.at/dZ018
👉Paper arxiv.org/pdf/2404.12378.pdf
👉Project 6img-to-3d.github.io/
👉Code github.com/continental/6Img-to-3D
🪼 All You Need is SAM (+Flow) 🪼
👉Oxford unveils the new SOTA for moving object segmentation via SAM + Optical Flow. Two novel models & Source Code announced 💙
👉Review https://t.ly/ZRYtp
👉Paper https://lnkd.in/d4XqkEGF
👉Repo coming
👉Project https://lnkd.in/dHpmx3FF
🎲 Articulated Objs from MonoClips 🎲
👉REACTO is the new SOTA to address the challenge of reconstructing general articulated 3D objects from single monocular video
👉Review https://t.ly/REuM8
👉Paper https://lnkd.in/d6PWagij
👉Project https://lnkd.in/dpg3x4tm
👉Repo https://lnkd.in/dRZWj6_N
⚽ SoccerNET: Athlete Tracking & ID ⚽
👉SoccerNet Challenge is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field.
👉Review https://t.ly/Mdu9s
👉Paper arxiv.org/pdf/2404.11335.pdf
👉Code github.com/SoccerNet/sn-gamestate
🧤Neural MusculoSkeletal-MANO🧤
👉SJTU unveils MusculoSkeletal-MANO, novel musculoskeletal system with a learnable parametric hand model. Source Code announced 💙
👉Review https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2404.10227.pdf
👉Project https://ms-mano.robotflow.ai/
👉Code announced (no repo yet)
🪐YOLO-CIANNA: Neural Astro🪐
👉 CIANNA is a general-purpose deep learning framework for (but not only for) astronomical data analysis. Source Code released 💙
👉Review https://t.ly/441XS
👉Paper arxiv.org/pdf/2402.05925.pdf
👉Code github.com/Deyht/CIANNA
👉Wiki github.com/Deyht/CIANNA/wiki
☄️ Tracking Any 2D Pixels in 3D ☄️
👉 SpatialTracker lifts 2D pixels to 3D using monocular depth, represents the 3D content of each frame efficiently using a triplane representation, and performs iterative updates using a transformer to estimate 3D trajectories.
👉Review https://t.ly/B28Cj
👉Paper https://lnkd.in/d8ers_nm
👉Project https://lnkd.in/deHjtZuE
👉Code https://lnkd.in/dMe3TvFT
⚛️ Flying w/ Photons: Neural Render ⚛️
👉Novel neural rendering technique that seeks to synthesize videos of light propagating through a scene from novel, moving camera viewpoints. Pico-Seconds time resolution!
👉Review https://t.ly/ZqL3a
👉Paper arxiv.org/pdf/2404.06493.pdf
👉Project anaghmalik.com/FlyingWithPhotons/
👉Code github.com/anaghmalik/FlyingWithPhotons
🧞 XComposer2: 4K Vision-Language 🧞
👉InternLMXComposer2-4KHD brings LVLM resolution capabilities up to 4K HD (3840×1600) and beyond. Authors: Shanghai AI Lab, CUHK, SenseTime & Tsinghua. Source Code & Models released 💙
👉Review https://t.ly/GCHsz
👉Paper arxiv.org/pdf/2404.06512.pdf
👉Code github.com/InternLM/InternLM-XComposer
🧞🧞 XComposer2-4K: 4K Vision-Language 🧞🧞
👉InternLMXComposer2-4KHD brings LVLM resolution capabilities up to 4K HD (3840×1600) and beyond. Authors: Shanghai AI Lab, CUHK, SenseTime & Tsinghua. Source Code & Models released 💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Large Vision-Language Models (LVLMs) to 4K HD
✅Free-form Interleaved Text-Image Composition
✅Dynamic Resolution / Automatic Patch Config.
✅SOTA or competitive despite only 7B params
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2404.06512.pdf
👉Code github.com/InternLM/InternLM-XComposer
🔌 BodyMAP: human body & pressure 🔌
👉#Nvidia (+CMU) unveils BodyMAP, the new SOTA in predicting body mesh (3D pose & shape) and 3D applied pressure on the human body. Source Code released, Dataset coming 💙
👉Review https://t.ly/8926S
👉Project bodymap3d.github.io/
👉Paper https://lnkd.in/gCxH4ev3
👉Code https://lnkd.in/gaifdy3q
👗 Neural Bodies with Clothes 👗
👉Neural-ABC is a novel parametric model based on neural implicit functions that can represent clothed human bodies with disentangled latent spaces for identity, clothing, shape, and pose. Author: University of Science & Technology of China. Dataset & Source Code released 💙
👉Review https://t.ly/Un1wc
👉Project https://lnkd.in/dhDG6FF5
👉Paper https://lnkd.in/dhcfK7jZ
👉Code https://lnkd.in/dQvXWysP
👆 iSeg: Interactive 3D Segmentation 👆
👉 iSeg: interactive segmentation technique for 3D shapes operating entirely in 3D. It accepts both positive/negative clicks directly on the shape's surface, indicating inclusion & exclusion of regions.
👉Review https://t.ly/tyFnD
👉Paper https://lnkd.in/dydAz8zp
👉Project https://lnkd.in/de-h6SRi
👉Code (coming)
🕷️ Gen-NeRF2NeRF Translation 🕷️
👉GenN2N: unified NeRF-to-NeRF translation for editing tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc.
👉Review https://t.ly/VMWAH
👉Paper https://arxiv.org/pdf/2404.02788.pdf
👉Project https://xiangyueliu.github.io/GenN2N/
👉Code https://github.com/Lxiangyue/GenN2N
🔥 ECoDepth: SOTA Diffusive Mono-Depth 🔥
👉New SIDE model using a diffusion backbone conditioned on ViT embeddings. It's the new SOTA in SIDE. Source Code released 💙
👉Review https://t.ly/s2pbB
👉Paper https://lnkd.in/eYt5yr_q
👉Code https://lnkd.in/eEcyPQcd
🔘 RELI11D: Multimodal Humans 🔘
👉RELI11D is the ultimate and high-quality multimodal human motion dataset involving LiDAR, IMU system, RGB camera, and Event camera. Dataset & Source Code to be released soon💙
👉Review https://t.ly/5EG6X
👉Paper https://lnkd.in/ep6Utcik
👉Project https://lnkd.in/eDhNHYBb
🪼 Universal Mono Metric Depth 🪼
👉ETH unveils UniDepth: metric 3D scenes from solely single images across domains. A novel, universal and flexible MMDE solution. Source code released💙
👉Review https://t.ly/5C8eq
👉Paper arxiv.org/pdf/2403.18913.pdf
👉Code github.com/lpiccinelli-eth/unidepth
🪼 Universal Mono Metric Depth 🪼
👉ETH unveils UniDepth: metric 3D scenes from solely single images across domains. A novel, universal and flexible MMDE solution. Source code released💙
👉Review https://t.ly/5C8eq
👉Paper https://arxiv.org/pdf/2403.18913.pdf
👉Code https://github.com/lpiccinelli-eth/unidepth
