AI with Papers - Artificial Intelligence & Deep Learning
رفتن به کانال در Telegram
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 17 261 مشترک است و جایگاه 7 500 را در دسته فناوری و برنامه‌ها و رتبه 2 124 را در منطقه ماليزيا دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 261 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 24 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 258 و در ۲۴ ساعت گذشته برابر -2 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 16.76% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.11% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 2 893 بازدید دریافت می‌کند. در اولین روز معمولاً 1 227 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 16 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 25 اکتبر, 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.
17 261
مشترکین
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+27 روز
+25830 روز
آرشیو پست ها
🧿 Look Ma, no markers 🧿
👉#Microsoft unveils the first technique for marker-free, HQ reconstruction of COMPLETE human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware. Impressive results! Repo for training & Dataset released💙
👉Review https://t.ly/5fN0g
👉Paper arxiv.org/pdf/2410.11520
👉Project microsoft.github.io/SynthMoCap/
👉Repo github.com/microsoft/SynthMoCap
🔥BitNet: code of 1-bit LLM is out 🔥
👉BitNet by #Microsoft, announced in late 2023, is a 1-bit Transformer architecture designed for LLMs. BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Source Code just released a few hours ago 💙
👉Review https://t.ly/3G2LA
👉Paper arxiv.org/pdf/2310.11453
👉Code https://lnkd.in/duPADJVb
☀️ GS + Depth = SOTA ☀️
👉ETH unveils DepthSplat, the new SOTA in depth estimation and novel view synthesis tasks. The key feature is the cross-task interactions between Gaussian Splatting & depth estimation. Source Code to be released in a few days💙
👉Review https://t.ly/87HuH
👉Paper arxiv.org/abs/2410.13862
👉Project haofeixu.github.io/depthsplat/
👉Code github.com/cvg/depthsplat
🦠 Neural Metamorphosis 🦠
👉NU Singapore unveils NeuMeta to transform neural nets by allowing a single model to adapt on the fly to different sizes, generating the right weights when needed.
👉Review https://t.ly/DJab3
👉Paper arxiv.org/pdf/2410.11878
👉Project adamdad.github.io/neumeta
👉Code github.com/Adamdad/neumeta
🔥 CoTracker3 by #META is out! 🔥
👉#Meta (+VGG Oxford) unveils CoTracker3, a new tracker that outperforms the previous SoTA by a large margin using only the 0.1% of the training data 🤯🤯🤯
👉Review https://t.ly/TcRIv
👉Paper arxiv.org/pdf/2410.11831
👉Project cotracker3.github.io/
👉Code github.com/facebookresearch/co-tracker
🪞Robo-Emulation via Video Imitation🪞
👉OKAMI (UT & #Nvidia) is a novel foundation method that generates a manipulation plan from a single RGB-D video and derives a policy for execution.
👉Review https://t.ly/_N29-
👉Paper arxiv.org/pdf/2410.11792
👉Project https://lnkd.in/d6bHF_-s
🔥 DEPTH ANY VIDEO is out! 🔥
👉DAV is a novel foundation model for image/video depth estimation.The new SOTA for accuracy & consistency, up to 150 FPS!
👉Review https://t.ly/CjSz2
👉Paper arxiv.org/pdf/2410.10815
👉Project depthanyvideo.github.io/
👉Code github.com/Nightmare-n/DepthAnyVideo
🥎POKEFLEX: Soft Object Dataset🥎
👉PokeFlex from ETH is a dataset that includes 3D textured meshes, point clouds, RGB & depth maps of deformable objects. Pretrained models & dataset announced💙
👉Review https://t.ly/GXggP
👉Paper arxiv.org/pdf/2410.07688
👉Project https://lnkd.in/duv-jS7a
👉Repo
💡Diffusion Models Relighting💡
👉#Netflix unveils DifFRelight, a novel free-viewpoint facial relighting via diffusion model. Precise lighting control, high-fidelity relit facial images from flat-lit inputs.
👉Review https://t.ly/fliXU
👉Paper arxiv.org/pdf/2410.08188
👉Project www.eyelinestudios.com/research/diffrelight.html
🥦Gaussian Splatting VTON🥦
👉GS-VTON is a novel image-prompted 3D-VTON which, by leveraging 3DGS as the 3D representation, enables the transfer of pre-trained knowledge from 2D VTON models to 3D while improving cross-view consistency. Code announced💙
👉Review https://t.ly/sTPbW
👉Paper arxiv.org/pdf/2410.05259
👉Project yukangcao.github.io/GS-VTON/
👉Repo github.com/yukangcao/GS-VTON
🐏 EFM3D: 3D Ego-Foundation 🐏
👉#META presents EFM3D, the first benchmark for 3D object detection and surface regression on HQ annotated egocentric data of Project Aria. Datasets & Code released💙
👉Review https://t.ly/cDJv6
👉Paper arxiv.org/pdf/2406.10224
👉Project www.projectaria.com/datasets/aeo/
👉Repo github.com/facebookresearch/efm3d
🔥 "Deep Gen-AI" Full Course 🔥
👉A fresh course from Stanford about the probabilistic foundations and algorithms for deep generative models. A novel overview about the evolution of the genAI in #computervision, language and more...
👉Review https://t.ly/ylBxq
👉Course https://lnkd.in/dMKH9gNe
👉Lectures https://lnkd.in/d_uwDvT6
🛳️ EVER Ellipsoid Rendering 🛳️
👉UCSD & Google present EVER, a novel method for real-time differentiable emission-only volume rendering. Unlike 3DGS it does not suffer from popping artifacts and view dependent density, achieving ∼30 FPS at 720p on #NVIDIA RTX4090.
👉Review https://t.ly/zAfGU
👉Paper arxiv.org/pdf/2410.01804
👉Project half-potato.gitlab.io/posts/ever/
🦴 One-Image Object Detection 🦴
👉Delft University (+Hensoldt Optronics) introduces OSSA, a novel unsupervised domain adaptation method for object detection that utilizes a single, unlabeled target image to approximate the target domain style. Code released💙
👉Review https://t.ly/-li2G
👉Paper arxiv.org/pdf/2410.00900
👉Code github.com/RobinGerster7/OSSA
🍇SPARK: Real-time Face Capture🍇
👉Technicolor Group unveils SPARK, a novel high-precision 3D face capture via collection of unconstrained videos of a subject as prior information. New SOTA able to handle unseen pose, expression and lighting. Impressive results. Code & Model announced💙
👉Review https://t.ly/rZOgp
👉Paper arxiv.org/pdf/2409.07984
👉Project kelianb.github.io/SPARK/
👉Repo github.com/KelianB/SPARK/
👩🦰 SOTA Gaussian Haircut 👩🦰
👉ETH et. al unveils Gaussian Haircut, the new SOTA in hair reconstruction via dual representation (classic + 3D Gaussian). Code and Model announced💙
👉Review https://t.ly/aiOjq
👉Paper arxiv.org/pdf/2409.14778
👉Project https://lnkd.in/dFRm2ycb
👉Repo https://lnkd.in/d5NWNkb5
🌾 New SOTA Edge Detection 🌾
👉CUP (+ ESPOCH) unveils the new SOTA for Edge Detection (NBED); superior performance consistently across multiple benchmarks, even compared with huge computational cost and complex training models. Source Code released💙
👉Review https://t.ly/zUMcS
👉Paper arxiv.org/pdf/2409.14976
👉Code github.com/Li-yachuan/NBED
🩰 Dressed Humans in the wild 🩰
👉ETH (+ #Microsoft ) ReLoo: novel 3D-HQ reconstruction of humans dressed in loose garments from mono in-the-wild clips. No prior assumptions about the garments. Source Code announced, coming 💙
👉Review https://t.ly/evgmN
👉Paper arxiv.org/pdf/2409.15269
👉Project moygcc.github.io/ReLoo/
👉Code github.com/eth-ait/ReLoo
🎢 Robo-quadruped Parkour🎢
👉LAAS-CNRS unveils a novel RL approach to perform agile skills that are reminiscent of parkour, such as walking, climbing high steps, leaping over gaps, and crawling under obstacles. Data and Code available💙
👉Review https://t.ly/-6VRm
👉Paper arxiv.org/pdf/2409.13678
👉Project gepetto.github.io/SoloParkour/
👉Code github.com/Gepetto/SoloParkour
🌏 JoyHallo: Mandarin Digital Human 🌏
👉JD Health faced the challenges of audio-driven video generation in Mandarin, a task complicated by the language’s intricate lip movements and the scarcity of HQ datasets. Impressive results (-> audio ON). Code Models available💙
👉Review https://t.ly/5NGDh
👉Paper arxiv.org/pdf/2409.13268
👉Project jdh-algo.github.io/JoyHallo/
👉Code github.com/jdh-algo/JoyHallo
