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
🛸Real-Time Differentiable Tracing🛸
👉 Radiant Foam is a novel scene representation by leveraging the decades-old efficient volumetric mesh ray tracing algorithm (largely overlooked in recent research). Performing like Gaussian Splatting, without the constraints of rasterization. Code announced💙
👉Review https://shorturl.at/26U06
👉Paper https://arxiv.org/pdf/2502.01157
👉Project https://radfoam.github.io/
👉Repo https://github.com/theialab/radfoam
🐙MambaGlue: SOTA feats. matching🐙
👉MambaGlue is a hybrid neural network combining the Mamba and the Transformer architectures to match local features. Source Code announced, to be released💙
👉Review https://shorturl.at/LxDG1
👉Paper arxiv.org/pdf/2502.00462
👉Repo https://lnkd.in/dAujfGZQ
🈯 SOTA 0-Shot Multi-View 🈯
👉MVGD by #TOYOTA is the SOTA method that generates images and scale-consistent depth maps from novel viewpoints given an arbitrary number of posed input views. A novel diffusion-based architecture capable of direct pixel-level generation. Code announced 💙
👉Review https://t.ly/_ecKl
👉Paper arxiv.org/pdf/2501.18804
👉Project mvgd.github.io/
👉Repo TBA
💎AI-driven Docs Conversion💎
👉Docling by IBM, is the ALL-in-ONE, open source solution for documents; parsing several types of popular formats into a unified, richly structured representation. Powered by SOTA models for layout (DocLayNet) and table structure (TableFormer), it runs efficiently on low-cost hardware. Code under MIT💙
👉Review https://t.ly/nSCfT
👉Paper https://lnkd.in/dc5Kpc2F
👉Repo https://lnkd.in/d9gvw9bt
Social feed of everyone is broken because of unnecessary/not required opinions about DeepSeek. Your wish:
🌅 Generative Human Mesh Recovery 🌅
👉GenHMR is a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in 2D-to-3D mapping process. Impressive results but no code announced 🥺
👉Review https://t.ly/Rrzpj
👉Paper https://arxiv.org/pdf/2412.14444
👉Project m-usamasaleem.github.io/publication/GenHMR/GenHMR.html
☀️ Relightable Full-Body Avatars ☀️
👉#Meta unveils the first approach ever to jointly model the relightable appearance of the body, face, and hands of drivable avatars.
👉Review https://t.ly/kx9gf
👉Paper arxiv.org/pdf/2501.14726
👉Project neuralbodies.github.io/RFGCA
🦕[SOTA] Visual Grounding VOS🦕
👉ReferDINO is the first end-to-end approach for adapting foundational visual grounding models to RVOS. Code & models to be released soon💙
👉Review https://t.ly/SDFy9
👉Paper arxiv.org/pdf/2501.14607
👉Project isee-laboratory.github.io/ReferDINO/
👉Repo github.com/iSEE-Laboratory/ReferDINO
🎨MatAnyone: Human Matting🎨
👉MatAnyone is a novel approach for human video matting that supports the target assignment. Stable tracking in long videos even with complex/ambiguous BGs. Code & 🤗-Demo announced💙
👉Review https://t.ly/NVXsT
👉Paper arxiv.org/pdf/2501.14677
👉Project pq-yang.github.io/projects/MatAnyone
👉Repo TBA
🪆SOTA Points Segmentation🪆
👉VGG Oxford unveils a novel loss to segment objects in videos based on their motion and NO other forms of supervision! Training the net using long-term point trajectories as a supervisory signal to complement optical flow. New SOTA!
👉Review https://t.ly/8Bsbt
👉Paper https://arxiv.org/pdf/2501.12392
👉Code https://github.com/karazijal/lrtl
👉Project www.robots.ox.ac.uk/~vgg/research/lrtl/
🔥 The code of DynOMo is out 🔥
👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input
👉Review https://t.ly/t5pCf
👉Paper https://lnkd.in/dwhzz4_t
👉Repo github.com/dvl-tum/DynOMo
👉Project https://lnkd.in/dMyku2HW
🔥 The code of DynOMo is out 🔥
👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input. Source code released under BSD 3-Clause💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅TUM, CMU (H/T Jenny Seidenschwarz) & NVIDIA
✅Online 2D/3D point tracking from unposed monocular
✅Tracking-by-reconstruction baseline for online TAP
✅New baseline for online PT with unposed mono-cam
hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse hashtag#LLM
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://lnkd.in/dwhzz4_t
👉Repo github.com/dvl-tum/DynOMo
👉Project https://lnkd.in/dMyku2HW
🦠A-Life with Foundation Models🦠
👉A super team unveils ASAL, a new paradigm for Artificial Life research. A diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia & Neural Cellular Automata. Code under Apache 2.0💙
👉Review https://t.ly/7SZ8A
👉Paper arxiv.org/pdf/2412.17799
👉Project http://pub.sakana.ai/asal/
👉Repo https://lnkd.in/dP5yxKtw
🎤EMO2: Audio-Driven Avatar🎤
👉Alibaba previews a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Turn your audio ON. Stunning results but no code 🥺
👉Review https://t.ly/x8slQ
👉Paper arxiv.org/pdf/2501.10687
👉Project humanaigc.github.io/emote-portrait-alive-2/
👉Repo 🥺
🧵Time-Aware Pts-Tracking🧵
👉Chrono: feature backbone specifically designed for point tracking with built-in temporal awareness. Long-term temporal context, enabling precise prediction even without the refinements. Code announced💙
👉Review https://t.ly/XAL7G
👉Paper arxiv.orgzpdf/2501.12218
👉Project cvlab-kaist.github.io/Chrono/
👉Repo github.com/cvlab-kaist/Chrono
🔥 [SOTA] Long-Video Depth Anything 🔥
👉ByteDance unveils Video Depth Anything: HQ, consistent depth estimation in SUPER-long videos (over several minutes) without sacrificing efficiency. Based on Depth Anything V2 with a novel efficient spatial-temporal head. Repo available under Apache 2.0💙
👉Review https://t.ly/Q4ZZd
👉Paper arxiv.org/pdf/2501.12375
👉Project https://lnkd.in/dKNwJzbM
👉Repo https://lnkd.in/ddfwwpCj
🌈 #Nvidia Foundation ZS-Stereo 🌈
👉Nvidia unveils FoundationStereo, a foundation model for stereo depth estimation with strong zero-shot generalization. In addition, a large-scale (1M stereo pairs) synthetic training dataset featuring large diversity and high photorealism. Code, model & dataset to be released💙
👉Review https://t.ly/rfBr5
👉Paper arxiv.org/pdf/2501.09898
👉Project nvlabs.github.io/FoundationStereo/
👉Repo github.com/NVlabs/FoundationStereo/tree/master
🧽 Diffusion Video Inpainting 🧽
👉#Alibaba unveils a technical report about DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. Code & weights released under Apache💙
👉Review https://t.ly/7rEll
👉Paper arxiv.org/pdf/2501.10018
👉Project lixiaowen-xw.github.io/DiffuEraser-page/
👉Repo github.com/lixiaowen-xw/DiffuEraser
🏄♀️ GSTAR: Gaussian Surface Tracking 🏄♀️
👉ETH Zurich unveils GSTAR, a novel framework for photo-realistic rendering, surface reconstruction, and 3D tracking for dynamic scenes while handling topology changes. Code announced💙
👉Review https://t.ly/udpMq
👉Paper arxiv.org/pdf/2501.10283
👉Project chengwei-zheng.github.io/GSTAR/
👉Repo TBA
🎁Free Book: LLM Foundations🎁
👉A fully free book just released on arXiv to outline the basic concepts of #LLMs and related techniques with a focus on the foundational aspects.
✅Chapter 1: basics of pre-training
✅Chapter 2: gen-models & LLMs
✅Chapter 3: prompting methods
✅Chapter 4: alignment methods
👉If you have any background in ML, along with a certain understanding of stuff like Transformers, this book will be "smooth". However, even without this prior knowledge, it is still perfectly fine because the contents of each chapter are self-contained.
👉Review https://t.ly/9LGCa
👉Book https://lnkd.in/d3VkswZf
