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 7 509 را در دسته فناوری و برنامه‌ها و رتبه 2 130 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 25 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 261 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 16.91% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.04% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 2 918 بازدید دریافت می‌کند. در اولین روز معمولاً 1 216 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 26 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
+224 ساعت
+17 روز
+26130 روز
آرشیو پست ها
👆 iSeg: Interactive 3D Segmentation 👆
👉 iSeg: interactive segmentation technique for 3D shapes operating entirely in 3D. It accepts both positive/negative clicks directly on the shape's surface, indicating inclusion & exclusion of regions.
👉Review https://t.ly/tyFnD
👉Paper https://lnkd.in/dydAz8zp
👉Project https://lnkd.in/de-h6SRi
👉Code (coming)
🕷️ Gen-NeRF2NeRF Translation 🕷️
👉GenN2N: unified NeRF-to-NeRF translation for editing tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc.
👉Review https://t.ly/VMWAH
👉Paper https://arxiv.org/pdf/2404.02788.pdf
👉Project https://xiangyueliu.github.io/GenN2N/
👉Code https://github.com/Lxiangyue/GenN2N
🔥 ECoDepth: SOTA Diffusive Mono-Depth 🔥
👉New SIDE model using a diffusion backbone conditioned on ViT embeddings. It's the new SOTA in SIDE. Source Code released 💙
👉Review https://t.ly/s2pbB
👉Paper https://lnkd.in/eYt5yr_q
👉Code https://lnkd.in/eEcyPQcd
🔘 RELI11D: Multimodal Humans 🔘
👉RELI11D is the ultimate and high-quality multimodal human motion dataset involving LiDAR, IMU system, RGB camera, and Event camera. Dataset & Source Code to be released soon💙
👉Review https://t.ly/5EG6X
👉Paper https://lnkd.in/ep6Utcik
👉Project https://lnkd.in/eDhNHYBb
🪼 Universal Mono Metric Depth 🪼
👉ETH unveils UniDepth: metric 3D scenes from solely single images across domains. A novel, universal and flexible MMDE solution. Source code released💙
👉Review https://t.ly/5C8eq
👉Paper arxiv.org/pdf/2403.18913.pdf
👉Code github.com/lpiccinelli-eth/unidepth
🪼 Universal Mono Metric Depth 🪼
👉ETH unveils UniDepth: metric 3D scenes from solely single images across domains. A novel, universal and flexible MMDE solution. Source code released💙
👉Review https://t.ly/5C8eq
👉Paper https://arxiv.org/pdf/2403.18913.pdf
👉Code https://github.com/lpiccinelli-eth/unidepth
💦 ObjectDrop: automagical objects removal 💦
👉#Google unveils ObjectDrop, the new SOTA in photorealistic object removal and insertion. Focus on shadows and reflections, impressive!
👉Review https://t.ly/ZJ6NN
👉Paper https://arxiv.org/pdf/2403.18818.pdf
👉Project https://objectdrop.github.io/
🏀 MAVOS Object Segmentation 🏀
👉MAVOS is a transformer-based VOS that introduces a novel, optimized and dynamic long-term modulated cross-attention memory. Code & Models announced (coming soon under BSD 3-Clause)💙
👉Review https://t.ly/SKaRG
👉Paper https://lnkd.in/dQyifKa3
👉Project github.com/Amshaker/MAVOS
👉Code/Demo (announced)
☔ AiOS: All-in-One-Stage Humans ☔
👉All-in-one-stage framework for SOTA multiple expressive pose and shape recovery without additional human detection step.
👉Review https://t.ly/ekNd4
👉Paper https://arxiv.org/pdf/2403.17934.pdf
👉Project https://ttxskk.github.io/AiOS/
👉Code/Demo (announced)
💄TinyBeauty: 460 FPS Diffusion Make-up💄
👉TinyBeauty: only 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile!
👉Review https://t.ly/LG5ok
👉Paper https://arxiv.org/pdf/2403.15033.pdf
👉Project https://tinybeauty.github.io/TinyBeauty/
💄💄TinyBeauty: 460 FPS Diffusion Make-up💄💄
👉TinyBeauty;:necessitates merely 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile! Authors: Jiao Tong University, Alibaba, USC-SJTU.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DAL, Data Amplify Learning: novel learning framework
✅Diffusion-based Data Amplifier for better training
✅Only 80K parameters to achieve the previous SOTA
✅Insane inference speed (460 fps) on iPhone 13
✅Highly competitive using only FIVE image pairs
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2403.15033.pdf
👉Project https://tinybeauty.github.io/TinyBeauty/
🦖 T-Rex 2: a new SOTA is out! 🦖
👉A novel (VERY STRONG) open-set object detector model. Strong zero-shot capabilities, suitable for various scenarios with only one suit of weights. Demo and Source Code released💙
👉Review https://t.ly/fYw8D
👉Paper https://lnkd.in/dpmRh2zh
👉Project https://lnkd.in/dnR_jPcR
👉Code https://lnkd.in/dnZnGRUn
👉Demo https://lnkd.in/drDUEDYh
🦕 DINO-based Video Tracking 🦕
👉The Weizmann Institute announced the new SOTA in point-tracking via pre-trained DINO features. Source code announced (not yet released)💙
👉Review https://t.ly/_GIMT
👉Paper https://lnkd.in/dsGVDcar
👉Project dino-tracker.github.io/
👉Code (announced)
🪼FaceXFormer: Unified Face-Transformer🪼
👉FaceXFormer, the first unified transformer for facial analysis: face parsing, landmark detection, head pose, attributes recognition, age, gender, race, and landmarks.
👉Review https://t.ly/MfAFI
👉Paper https://arxiv.org/pdf/2403.12960.pdf
👉Project kartik-3004.github.io/facexformer_web/
👉Code github.com/Kartik-3004/facexformer
🏷️ Face Foundation Model 🏷️
👉Arc2Face, the first foundation model for human faces. Large dataset of high-resolution faces with consistent ID / intra-class variability, and an ID-conditioned face model trained on it. Source Code released 💙
👉Review https://t.ly/MfAFI
👉Paper https://lnkd.in/dViE_tCd
👉Project https://lnkd.in/d4MHdEZK
👉Code https://lnkd.in/dv9ZtDfA
🏷️🏷️Arc2Face: Face Foundation Model🏷️🏷️
👉Arc2Face, the first foundation model for human faces. Large dataset of high-resolution faces with consistent ID / intra-class variability, and an ID-conditioned face model trained on it. Source Code released 💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://lnkd.in/dViE_tCd
👉Project https://lnkd.in/d4MHdEZK
👉Code https://lnkd.in/dv9ZtDfA
🪖RT Humanoid from Head-Mounted Sensors🪖
👉#META (+CMU) announced SimXR, a method for controlling a simulated avatar from info obtained from AR/VR headsets
👉Review https://t.ly/Si2Mp
👉Paper arxiv.org/pdf/2403.06862.pdf
👉Project www.zhengyiluo.com/SimXR/
👺 Can GPT-4 play DOOM? 👺
👉Apparently yes, GPT-4 can play the game to a passable degree: it is able to manipulate doors, combat enemies, and perform pathing. Code (with licensing restrictions) released
👉Review https://t.ly/W8-0F
👉Paper https://lnkd.in/dmsB7bjA
👉Project https://lnkd.in/ddDPwjQB
🏛️ PIXART-Σ: 4K Generation 🏛️
👉PixArt-Σ is a novel Diffusion Transformer model (DiT) capable of directly generating images at 4K resolution. Authors: #Huawei, Dalian, HKU & HKUST. Demos available, code announced 💙
👉Review https://t.ly/Cm2Qh
👉Paper arxiv.org/pdf/2403.04692.pdf
👉Project pixart-alpha.github.io/PixArt-sigma-project/
👉Repo (empty) github.com/PixArt-alpha/PixArt-sigma
🤗-Demo https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha
