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 17 262 مشترک است و جایگاه 7 491 را در دسته فناوری و برنامه‌ها و رتبه 2 128 را در منطقه ماليزيا دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 262 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 23 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 255 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 18.93% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.17% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 3 267 بازدید دریافت می‌کند. در اولین روز معمولاً 1 237 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 20 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 24 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.
17 262
مشترکین
+224 ساعت
+67 روز
+25530 روز
آرشیو پست ها
🔪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
🦗Character Mixing Generation🦗
👉MBZUAI unveils the first ever video-gen system able to preserve character ID, behavior & original style while generating plausible interactions between characters that have never coexisted - from cartoons (We Bare Bears, Tom & Jerry) to realistic humans (Mr. Bean, Young Sheldon)
👉Review https://t.ly/tN84a
👉Paper https://lnkd.in/dhKMwukv
👉Project https://lnkd.in/dBkJs48h
👉Repo https://lnkd.in/dw_uzgAk
🐠ITTO: Protocol for Dynamic Tracking🐠
👉ITTO by Caltech is a novel long-range tracking benchmark suite for evaluating and diagnosing tracking methods on complex and long-range motions. Repo under CC BY-NC 4.0💙
👉Review https://t.ly/tN84a
👉Paper https://arxiv.org/pdf/2510.19819
👉Project https://glab-caltech.github.io/ITTO/
👉Repo https://github.com/ilonadem/itto
🏜️Omni Driving Navigation Models🏜️
👉OmniNWM is a unified panoramic navigation world model that advances autonomous driving by jointly generating multi-modal states (RGB, semantics, depth, 3D occupancy), enabling precise action control & facilitating closed-loop evaluation through occupancy-based dense rewards. Repo under Apache 2.0💙
👉Review https://t.ly/ktXvz
👉Paper https://lnkd.in/eFKSZnrc
👉Project https://lnkd.in/eSDfccv8
👉Repo https://lnkd.in/efCSvjtp
Repo (pretty empty) now online: https://github.com/OatmealLiu/UrbanVerse
🔥 SAM 2++: Track Anything 🔥
👉SAM 2++ is a novel unified model towards tracking at any granularity, including masks, boxes, and points. Impressive results but no code announced yet 😢
👉Review https://t.ly/I392_
👉Paper arxiv.org/pdf/2510.18822
👉Project tracking-any-granularity.github.io/
👉Repo :(
+2
🌵All-in-One Dense Keypoints🌵
👉DeepDetect is a novel all-in-one, dense keypoints detector that unifies the strengths of SIFT, ORB, BRISK, FAST, AGAST, Harris, Shi-Tomasi, Canny & Sobel into a neural net. DAMN ROMANTIC. Repo under MIT💙
👉Review https://t.ly/VKGct
👉Paper https://arxiv.org/pdf/2510.17422
👉Repo https://github.com/saktx/DeepDetect
🦄 City-Tour -> Simulation 🦄
👉UrbanVerse is a novel system to convert real-world urban scenes from city-tour videos into physics-aware, interactive simulation environments, enabling scalable robot learning in urban spaces with real-world generalization. Repo & Data announced 💙
👉Review https://t.ly/UvXNS
👉Paper https://arxiv.org/pdf/2510.15018
👉Project https://urbanverseproject.github.io/
👉Repo TBA
🫙Universal Feature Up-Sampling🫙
👉AnyUp is a novel method for feature up-sampling that can be applied to ANY vision feature at ANY resolution, without encoder-specific training: inference-time feature-agnostic up-sampling architecture to improve up-sampling quality. Repo under CC-4.0💙
👉Review https://t.ly/HvEw9
👉Paper https://arxiv.org/pdf/2510.12764
👉Project https://wimmerth.github.io/anyup/
👉Repo https://github.com/wimmerth/anyup
🫧🫧 Detect Anything via MLLM 🫧🫧
👉Rex-Omni is a 3B-multimodal model that unifies visual perception tasks, including object detection, OCR, pointing, key-pointing & visual prompting into a single next point prediction framework. Impressive results. Full repo under IDEA License 1.0💙
👉Review https://t.ly/DCTk_
👉Paper https://lnkd.in/d4VDD-9j
👉Project https://lnkd.in/d6unEyvq
👉Repo https://lnkd.in/dkYJFe-x
↗️ TrackVLA++ Visual Tracking↘️
👉TrackVLA++ is a novel Vision-Language-Action model that incorporates spatial reasoning and target identification memory, enabling SOTA performance in both long-horizon and highly crowded tracking scenarios. Model announced💙
👉Review https://t.ly/ruYzc
👉Paper https://arxiv.org/pdf/2510.07134
👉Project pku-epic.github.io/TrackVLA-plus-plus-Web/
👉Repo TBA
💄Pixel-Perfect Depth (SOTA)💄
👉Pixel-Perfect Depth is a mono-depth estimation model with pixel-space diffusion transformers. New SOTA. Repo under Apache 2.0💙
👉Review https://t.ly/75PGo
👉Paper https://lnkd.in/d8wxFpyY
👉Project https://lnkd.in/dV5HhsqH
👉Repo https://lnkd.in/d9JKFBJq
👉Demo https://lnkd.in/d3wBkKJ9
🎺Visual Grounding RVOS🎺
👉ReferDINO is a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception & cross-modal spatio-temporal reasoning. Code, Demo & checkpoints released💙
👉Review https://t.ly/rOdkP
👉Paper https://lnkd.in/efuAFQdE
👉Project https://lnkd.in/dK3wMZqv
👉Repo https://lnkd.in/d3i2PsNF
🎺Visual Grounding RVOS🎺
👉ReferDINO is a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception & cross-modal spatio-temporal reasoning. Code, Demo & checkpoints released💙
👉Review https://t.ly/rOdkP
👉Paper https://lnkd.in/efuAFQdE
👉Project https://lnkd.in/dK3wMZqv
👉Repo https://lnkd.in/d3i2PsNF
