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
🫅FlowMDM: Human Composition🫅
👉FlowMDM, a diffusion-based approach capable of generating seamlessly continuous sequences of human motion from textual descriptions.
👉Review https://t.ly/pr2g_
👉Paper https://lnkd.in/daYRftdF
👉Project https://lnkd.in/dcRkv5Pc
👉Repo https://lnkd.in/dw-3JJks
🗃️ MATH-Vision Dataset 🗃️
👉MATH-V is a curated dataset of 3,040 HQ mat problems with visual contexts sourced from real math competitions. Dataset released 💙
👉Review https://t.ly/gmIAu
👉Paper arxiv.org/pdf/2402.14804.pdf
👉Project mathvision-cuhk.github.io/
👉Code github.com/mathvision-cuhk/MathVision
🩻 Pose via Ray Diffusion 🩻
👉Novel distributed representation of camera pose that treats a camera as a bundle of rays. Naturally suited for set-level transformers, it's the new SOTA on camera pose estimation. Source code released 💙
👉Review https://t.ly/qBsFK
👉Paper arxiv.org/pdf/2402.14817.pdf
👉Project jasonyzhang.com/RayDiffusion
👉Code github.com/jasonyzhang/RayDiffusion
🦥Neuromorphic Video Binarization🦥
👉 University of HK unveils the new SOTA in event-based neuromorphic binary reconstruction: stunning results on QR Code, barcode, & Text. Real-Time, only CPU, up to 10,000 FPS!
👉Review https://t.ly/V-NFa
👉Paper arxiv.org/pdf/2402.12644.pdf
👉Project github.com/eleboss/EBR
🪟 BOG: Fine Geometric Viewshttps://t.ly/E6T0W 🪟
👉 #Google (+Tübingen) unveils Binary Opacity Grids, a novel method to reconstruct triangle meshes from multi-view images able to capture fine geometric detail such as leaves, branches & grass. New SOTA, real-time on Google Pixel 8 Pro (and similar).
👉Review https://t.ly/E6T0W
👉Paper https://lnkd.in/dQEq3zy6
👉Project https://lnkd.in/dYYCadx9
👉Demo https://lnkd.in/d92R6QME
☀️ One2Avatar: Pic -> 3D Avatar ☀️
👉#Google presents a new approach to generate animatable photo-realistic avatars from only a few/one image. Impressive results.
👉Review https://t.ly/AS1oc
👉Paper arxiv.org/pdf/2402.11909.pdf
👉Project zhixuany.github.io/one2avatar_webpage/
🔥 Code is out 🔥
👉Repo: https://github.com/princeton-computational-imaging/NSF
🔥 Breaking: GEMINI 1.5 is out 🔥
👉Gemini 1.5 just announced: standard 128,000 token context window, up to 1 MILLION tokens via AI-Studio and #Vertex AI in private preview 🫠
👉Review https://t.ly/Vblvx
👉More: https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/#build-experiment
🆔 Magic-Me: ID-Specific Video 🆔
👉#ByteDance VCD: with just a few images of a specific identity it can generate temporal consistent videos aligned with the given prompt
👉Review https://t.ly/qjJ2O
👉Paper arxiv.org/pdf/2402.09368.pdf
👉Project magic-me-webpage.github.io
👉Code github.com/Zhen-Dong/Magic-Me
🍇 Graph Neural Network in TF 🍇
👉#Google released TensorFlow-GNN: a novel library to build Graph Neural Networks on the TensorFlow platform. Source Code released under Apache 2.0 license 💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Review https://t.ly/TQfg-
👉Code https://github.com/tensorflow/gnn
👉Blog https://blog.research.google/2024/02/graph-neural-networks-in-tensorflow.html
🌴 Direct-a-Video Generation 🌴
👉Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion
👉Review https://t.ly/dZSLs
👉Paper arxiv.org/pdf/2402.03162.pdf
👉Project https://direct-a-video.github.io/
🌆EfficientViT-SAM: 69x Faster SAM 🌆
👉EfficientViT-SAM is a new family of accelerated Segment Anything Models. The same old SAM’s lightweight prompt encoder and mask decoder, while replacing the heavy image encoder with EfficientViT. Up to 69x faster, source code released 💙 Authors: Tsinghua, MIT & #Nvidia💥
👉Review https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2402.05008.pdf
👉Code github.com/mit-han-lab/efficientvit
🌵 G-Splatting Controllable Portraits 🌵
👉From monocular/casual video captures, Rig3DGS rigs 3D Gaussian Splatting to enable the creation of re-animatable portrait videos with control over facial expressions, head-pose and viewing direction. Authors: Stony Brook University & #Adobe
👉Review https://t.ly/fq71w
👉Paper https://arxiv.org/pdf/2402.03723.pdf
👉Project shahrukhathar.github.io/2024/02/05/Rig3DGS.html
🪵 HASSOD Object Detection 🪵
👉 HASSOD: fully self-supervised detection and instance segmentation. The new SOTA able to understand the part-to-whole object composition like humans do.
👉Review https://t.ly/66qHF
👉Paper arxiv.org/pdf/2402.03311.pdf
👉Project hassod-neurips23.github.io/
👉Repo github.com/Shengcao-Cao/HASSOD
💥 #Py4AI: 2x speakers, 2x tickets! 💥
✅Doubling the speakers (6 -> 12!)
✅Adding a new track (2 tracks in parallel)
✅Releasing a new batch of 100 tickets!
👉 More: https://t.ly/WmVrM
🏇Bootstrapping TAP 🏇
👉#Deepmind shows how large-scale, unlabeled, uncurated real-world data can improve TAP with minimal architectural changes, via a self-supervised student-teacher setup. Source Code released 💙
👉Review https://t.ly/-S_ZL
👉Paper https://arxiv.org/pdf/2402.00847.pdf
👉Code https://lnkd.in/gyi7Dhkn
🍬 ABS: SOTA collision-free 🍬
👉ABS (Agile But Safe): learning-based control framework for agile and collision-free locomotion for quadrupedal robot. Source Code announced (coming) 💙
👉Review https://t.ly/AYu-Z
👉Paper arxiv.org/pdf/2401.17583.pdf
👉Project agile-but-safe.github.io/
👉Repo github.com/LeCAR-Lab/ABS
🚦(adding) Anything in Any Video🚦🚦
👉 XPeng Motors announced Anything in Any Scene: novel #AI for realistic video simulation that seamlessly inserts any object into an existing dynamic video. Strong emphasis on realism, the objects in the BBs don't exist. Source Code released 💙
👉Review https://t.ly/UYhl0
👉Code https://lnkd.in/gyi7Dhkn
👉Paper https://lnkd.in/gXyAJ6GZ
👉Project https://lnkd.in/gVA5vduD
🚦(adding) Anything in Any Video🚦🚦
👉 XPeng Motors announced Anything in Any Scene: novel #AI for realistic video simulation that seamlessly inserts any object into an existing dynamic video. Strong emphasis on realism, the objects in the BBs don't exist. Source Code released 💙
👉Review https://t.ly/UYhl0
👉Code https://lnkd.in/gyi7Dhkn
👉Paper https://lnkd.in/gXyAJ6GZ
👉Project https://lnkd.in/gVA5vduD
🎉 ADΔER: Event-Camera Suite 🎉
👉ADΔER: a novel/unified framework for event-based video. Encoder / transcoder / decoder for ADΔER (Address, Decimation, Δt Event Representation) video streams. Source code (RUST) released 💙 H/T author: Andrew C. Freeman from University of North Carolina, USA.
👉Review https://t.ly/w5_KC
👉Paper arxiv.org/pdf/2401.17151.pdf
👉Repo github.com/ac-freeman/adder-codec-rs
