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

📈 نظرة تحليلية على قناة تيليجرام AI with Papers - Artificial Intelligence & Deep Learning

تُعد قناة AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 17 154 مشتركاً، محتلاً المرتبة 7 726 في فئة التكنولوجيات والتطبيقات والمرتبة 2 240 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 154 مشتركاً.

بحسب آخر البيانات بتاريخ 21 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -166، وفي آخر 24 ساعة بمقدار -6، مع بقاء الوصول العام مرتفعاً.

حالة التحقق: غير موثّقة
معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 23.63‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 6.86‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
وصول المنشورات: يحصل كل منشور على متوسط 4 057 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 177 مشاهدة.
التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 26.
الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل framework, object, dataset, tba, depth.

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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”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 22 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

17 154

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-277 أيام

-16630 أيام

4 057

عرض المشاهدات

~ 1 17724 ساعات

~ 1 35348 ساعات

23.63%

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17 154

📫 MeshPose: DensePose + HMR 📫 👉MeshPose: novel approach to jointly tackle DensePose and Human Mesh Reconstruction in a while. A natural fit for #AR applications requiring real-time mobile inference. 👉Review https://t.ly/a-5uN 👉Paper https://arxiv.org/pdf/2406.10180 👉Project https://meshpose.github.io/

17 154

🎹 PianoMotion10M for gen-hands 🎹 👉PianoMotion10M: 116 hours of piano playing videos from a bird’s-eye view with 10M+ annotated hand poses. A big contributions in hand motion generation. Code & Dataset released💙 👉Review https://t.ly/_pKKz 👉Paper arxiv.org/pdf/2406.09326 👉Code https://lnkd.in/dcBP6nvm 👉Project https://lnkd.in/d_YqZk8x 👉Dataset https://lnkd.in/dUPyfNDA

17 154

🍉 MASA: MOT Anything By SAM 🍉 👉MASA: Matching Anything by Segmenting Anything pipeline to learn object-level associations from unlabeled images of any domain. An universal instance appearance model for matching any objects in any domain. Source code in June 💙 👉Review https://t.ly/pKdEV 👉Paper https://lnkd.in/dnjuT7xm 👉Project https://lnkd.in/dYbWzG4E 👉Code https://lnkd.in/dr5BJCXm

17 154

👑 Kling AI vs. OpenAI Sora 👑 👉Kling: the ultimate Chinese text-to-video model - rival to #OpenAI’s Sora. No papers or tech info to check, but stunning results from the official site. 👉Review https://t.ly/870DQ 👉Paper ??? 👉Project https://kling.kuaishou.com/

17 154

👗 SOTA Multi-Garment VTOn Editing 👗 👉#Google (+UWA) unveils M&M VTO, novel mix 'n' match virtual try-on that takes as input multiple garment images, text description for garment layout and an image of a person. It's the new SOTA both qualitatively and quantitatively. Impressive results! 👉Review https://t.ly/66mLN 👉Paper arxiv.org/pdf/2406.04542 👉Project https://mmvto.github.io

17 154

🧊 Universal 6D Pose/Tracking 🧊 👉Omni6DPose is a novel dataset for 6D Object Pose with 1.5M+ annotations. Extra: GenPose++, the novel SOTA in category-level 6D estimation/tracking thanks to two pivotal improvements. 👉Review https://t.ly/Ywgl1 👉Paper arxiv.org/pdf/2406.04316 👉Project https://lnkd.in/dHBvenhX 👉Lib https://lnkd.in/d8Yc-KFh

17 154

🚙 UA-Track: Uncertainty-Aware MOT🚙 👉UA-Track: novel Uncertainty-Aware 3D MOT framework which tackles the uncertainty problem from multiple aspects. Code announced, not released yet. 👉Review https://t.ly/RmVSV 👉Paper https://arxiv.org/pdf/2406.02147 👉Project https://liautoad.github.io/ua-track-website

17 154

📞FacET: VideoCall Change Your Expression📞 👉Columbia University unveils FacET: discovering behavioral differences between conversing face-to-face (F2F) and on video-calls (VCs). 👉Review https://t.ly/qsQmt 👉Paper arxiv.org/pdf/2406.00955 👉Project facet.cs.columbia.edu/ 👉Repo (empty) github.com/stellargo/facet

17 154

👹👹 AI and the Everything in the Whole Wide World Benchmark 👹👹 👉Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”. 👉Review https://t.ly/isdxM 👉Paper https://lnkd.in/dFraieZS 👉News https://lnkd.in/da-7PnVT

17 154

👹👹👹👹 Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”.

17 154

🐳 MultiPly: in-the-wild Multi-People 🐳 👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙 👉Review https://t.ly/_xjk_ 👉Paper arxiv.org/pdf/2406.01595 👉Project eth-ait.github.io/MultiPly 👉Repo github.com/eth-ait/MultiPly

17 154

🐳MultiPly: in-the-wild Multi-People from Mono🐳 👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙 #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2406.01595 👉Project https://eth-ait.github.io/MultiPly/ 👉Repo https://github.com/eth-ait/MultiPly

17 154

🎭New 2D Landmarks SOTA🎭 👉Flawless AI unveils FaceLift, a novel semi-supervised approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment, with no need for 3D landmark datasets. No code announced🥹 👉Review https://t.ly/lew9a 👉Paper arxiv.org/pdf/2405.19646 👉Project davidcferman.github.io/FaceLift

17 154

🧤 Transformer-based 4D Hands 🧤 👉4DHands is a novel and robust approach to recovering interactive hand meshes and their relative movement from monocular inputs. Authors: Beijing Normal University, Tsinghua & #Lenovo. No code announced yet 😢 👉Review https://t.ly/wvG-l 👉Paper arxiv.org/pdf/2405.20330 👉Project 4dhands.github.io/

17 154

🪰 Dynamic Gaussian Fusion via 4D Motion Scaffolds 🪰 👉MoSca is a novel 4D Motion Scaffolds to reconstruct/synthesize novel views of dynamic scenes from monocular videos in the wild! 👉Review https://t.ly/nSdEL 👉Paper arxiv.org/pdf/2405.17421 👉Code github.com/JiahuiLei/MoSca 👉Project https://lnkd.in/dkjMVcqZ

17 154

🦓 Z.S. Diffusive Segmentation 🦓 👉KAUST (+MPI) announced the first zero-shot approach for Video Semantic Segmentation (VSS) based on pre-trained diffusion models. Source Code released under MIT💙 👉Review https://t.ly/v_64K 👉Paper arxiv.org/pdf/2405.16947 👉Project https://lnkd.in/dcSt4dQx 👉Code https://lnkd.in/dcZfM8F3

17 154

⛈️Unsupervised Neuromorphic Motion⛈️⛈️ 👉The Western Sydney University unveils a novel unsupervised event-based motion segmentation algorithm, employing the #Prophesee Gen4 HD event camera. 👉Review https://t.ly/UZzIZ 👉Paper https://arxiv.org/pdf/2405.15209 👉Project https://samiarja.github.io/evairborne/ 👉Repo (empty) https://github.com/samiarja/ev/_deep/_motion_segmentation

17 154

🔥 YOLOv10 Object Detector is out 🔥 👉YOLOv10: novel real-time end-to-end object detection. Code released under GNU GPL v3.0💙 👉Review https://shorturl.at/ZIHBh 👉Paper arxiv.org/pdf/2405.14458 👉Code https://github.com/THU-MIG/yolov10/

17 154

🍀 OmniGlue: Foundation Matcher 🍀 👉#Google OmniGlue from #CVPR24: the first learnable image matcher powered by foundation models. Impressive out-of-domain results! 👉Review https://t.ly/ezaIc 👉Paper https://arxiv.org/pdf/2405.12979 👉Project hwjiang1510.github.io/OmniGlue/ 👉Code https://github.com/google-research/omniglue/

17 154

👚 ViViD: Diffusion Virtual Try-ON 👚 👉ViViD is a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Code announced, not released yet😢 👉Review https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2405.11794 👉Repo https://lnkd.in/dT4_bzPw 👉Project https://lnkd.in/dCK5ug4v