اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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 17 168 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 17 168 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.
17 168
مشترکین
اطلاعاتی وجود ندارد24 ساعت
-357 روز
-16930 روز
آرشیو پست ها
🦞Single Synthetic Image per Class🦞
👉MIT unveils Linear Gradient Matching (H/T Torralba), a novel method of distillation to use a single synthetic image per class for linear classifiers training (and more). Repo available💙
👉Review https://t.ly/dD3un
👉Paper arxiv.org/pdf/2511.16674
👉Project linear-gradient-matching.github.io/
👉Repo github.com/GeorgeCazenavette/linear-gradient-matching
🍕 Upsample Anything 🍕
👉Upsample Anything, a novel universal, training-free up-sampler via lightweight test-time optimization. No code but it's a relevant paper💙
👉Review https://t.ly/7LE6G
👉Paper https://lnkd.in/dsUfdtih
🍯Unwrapping of 3D Meshes🍯
👉PartUV is a novel part-based UV unwrapping method for 3D meshes; it combines learned part priors with geometric cues to generate a compact set of part-aligned charts. Repo released💙
👉Review https://t.ly/8dNIY
👉Paper arxiv.org/pdf/2511.16659
👉Project www.zhaoningwang.com/PartUV/
👉Repo github.com/EricWang12/PartUV
🔥 SAM 3/3D are OUT!! 🔥
👉#META released the SAM 3 family, a unified model for detection, segmentation, and tracking of objects in images & video using text, exemplar & visual prompts. Repo/Models under proprietary SAM license💙
👉Review https://t.ly/lnRZN
👉Paper https://t.ly/5tq9N
👉Project https://ai.meta.com/sam3/
👉Demo: https://segment-anything.com
👉Repo https://github.com/facebookresearch/sam3
⌚ Multi-Shot Video Segmentation ⌚
👉Fudan focuses on an underexplored task of multi-shot video object segmentation (MVOS). Benchmark and repo available (the extension part of SAM) under Apache 2.0💙
👉Review https://t.ly/WBW00
👉Paper https://arxiv.org/pdf/2511.13715
👉Project https://henghuiding.com/SAAS/
👉Repo https://github.com/FudanCVL/SAAS
🌩️ It's "Time-to-Move" 🌩️
👉Technion + Nvidia Time-to-Move (TTM) is a training-free, plug-and-play framework for motion- and appearance-controlled video generation with I2V diffusion models (Wan 2.2, CogVideoX, & Stable VD). Impressive results!
👉Review https://t.ly/0pwXm
👉Paper https://lnkd.in/dxD3uHYb
👉Project https://lnkd.in/dcE5juyM
👉Repo https://lnkd.in/dMMUjybJ
🔥Depth Anything 3 is out🔥
👉#ByteDance unveils Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from arbitrary visual inputs, with or without known camera poses. Repo under Apache 2.0💙
👉Review https://t.ly/AOPu7
👉Paper arxiv.org/pdf/2511.10647
👉Project https://lnkd.in/dnByyn2z
👉Repo https://lnkd.in/daCVz_4a
👉Demo https://lnkd.in/dKUZiJt
🟩 Foundational Humanoid 🟩
👉#NVIDIA unveils SONIC a novel foundational model for high-precision teleoperation & interactive control capabilities (running, jumping, crawling) with natural human-like movements. Code announced💙
👉Review https://t.ly/aUx_U
👉Paper https://lnkd.in/dctfShu8
👉Project https://lnkd.in/d_inmA2p
🚨 Announcement 🚨
I’ve received numerous reports of people blatantly copying my content on LinkedIn just to get a few likes.
Let me be very clear: I put a great deal of time and effort into reviewing papers and creating original, meaningful content. It’s disappointing to see professionals (some of whom are even members of this group or my connections) resorting to plagiarism instead of contributing their own ideas.
👉 Starting today, I’ll be removing these connections from LinkedIn and banning such individuals from this group.
📢 I also encourage everyone to report these cases whenever you come across them. Every single report helps stop this bad habit and keeps our community fair, respectful, and authentic.
🐼Pixel-Dense Embedding of Motion🐼
👉FlowFeat is a novel high-resolution and multi-task feature representation that embeds a distribution of plausible apparent motions, or motion profiles. Repo available under 💙
#artificialintelligence #AI #deeplearning #AIwithPapers
👉Review https://t.ly/aUx_U
👉Paper arxiv.org/pdf/2511.07696
👉Project tum-vision.github.io/flowfeat
👉Repo github.com/tum-vision/flowfeat
🎸Another BRIXEL in the Wall 🎸
👉BRIXEL allows the user to produce high-resolution feature maps using the DINOv3 backbone without requiring large amounts of compute. Repo released💙
👉Review https://t.ly/fZPwC
👉Paper arxiv.org/pdf/2511.05168
👉Repo github.com/alexanderlappe/BRIXEL
🔪Tracking Object Transformations🔪
👉"Track Any State": tracking objects through transformations while detecting/describing state changes. Repo & Dataset available under MIT💙
👉Review https://t.ly/NPyW4
👉Paper https://lnkd.in/d4pA3bXJ
👉Project https://lnkd.in/dgbNfCuj
👉Repo https://lnkd.in/dtVWq2z7
Greetings from the SMART CITY WORLD CONGRESS in Barcellona. If you are around, ping me ;)
👢Generative View Stitching 👢
👉GVS is a novel approach that enables collision-free camera-guided video generation for predefined trajectories, it's a non-autoregressive alternative to video length extrapolation. Full repo under MIT💙
👉Review https://t.ly/TiN_5
👉Paper https://arxiv.org/pdf/2510.24718
👉Project https://andrewsonga.github.io/gvs/
👉Repo github.com/andrewsonga/generative_view_stitching
🌱PlanarTrack: Large Planar Tracking🌱
👉PlanarTrack is a large-scale HQ and challenging benchmark for planar tracking: 1,150 sequences with 733K+ frames, including 1,000 short-term & 150 long-term videos. Repo & Dataset available💙
👉Review https://t.ly/mYNi7
👉Paper arxiv.org/pdf/2510.23368
👉Repo https://lnkd.in/edb3GMyT
👉Project https://lnkd.in/eC-hVB-U
👉Data https://lnkd.in/eew2j4tM
🦄Unified Region-Level MLLM🦄
👉PixeRefers is an unified multimodal LLM framework that supports precise, region-specific understanding in both static images and dynamic videos, overcoming the holistic, scene-level bias of prior MLLMs. SOTA results. Demo, Repo & Dataset available💙
👉Review https://t.ly/WH4dQ
👉Paper arxiv.org/pdf/2510.23603
👉Project circleradon.github.io/PixelRefer
👉Repo https://github.com/alibaba-damo-academy/PixelRefer
🧷Generative Point Tracking w/ FM🧷
👉Generative Point Tracker (GenPT) is a novel generative framework for modelling multi-modal trajectories. Able to capture the multi-modality in point trajectories. Repo under MIT💙
👉Review https://t.ly/MMFrt
👉Paper https://arxiv.org/pdf/2510.20951
👉Project mtesfaldet.net/genpt_projpage/
👉Repo https://github.com/tesfaldet/genpt
