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AI with Papers - Artificial Intelligence & Deep Learning

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

إظهار المزيد

📈 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 17 021 مشتركاً، محتلاً المرتبة 7 494 في فئة التكنولوجيات والتطبيقات والمرتبة 2 177 في منطقة ماليزيا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 021 مشتركاً.

بحسب آخر البيانات بتاريخ 25 أغسطس, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -24، وفي آخر 24 ساعة بمقدار 10، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 22.06‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً N/A‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 0 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 0.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل framework, object, dataset, tba, depth.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
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 26 أغسطس, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

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منشورات القناة
🍋‍🟩Remesh-Aware Mesh Deformation🍋‍🟩 👉RADmesh is a novel generative deformation technique enhanced by remeshing. Given a text prompt, it deforms and remeshes a mesh region to form new geometric features. Repo MIT💙 👉Review https://lnkd.in/p/eK6FZv9c 👉Paper https://arxiv.org/pdf/2608.17182 👉Project https://threedle.github.io/radmesh/ 👉Repo https://github.com/threedle/radmesh/

2
🐆 Anyone in 4D is out 🐆 👉4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstr
🐆 Anyone in 4D is out 🐆 👉4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstruction. Full repo under Apache 2.0💙 👉Review https://lnkd.in/p/ec4dzGvb 👉Paper https://arxiv.org/pdf/2608.20335 👉Project https://4danyone.github.io 👉Repo github.com/ant-research/4DAnyone
1 425
3
🔥🔥UPAL: Unified Points n' Lines🔥🔥 👉ETH (+Microsoft Spatial AI Lab) unveils a novel feature extractor that jointly extrac
🔥🔥UPAL: Unified Points n' Lines🔥🔥 👉ETH (+Microsoft Spatial AI Lab) unveils a novel feature extractor that jointly extracts keypoints, lines, and feature descriptors within a single lightweight net. SOTA in line detection can be achieved by adding only three convolutional layers to existing point extractor. Repo under Apache💙 👉Review https://lnkd.in/p/eW8j5JZj 👉Paper https://arxiv.org/pdf/2608.19894 👉Repo https://github.com/francois141/upal
1 689
4
🐠Dual-branch Elasticity ID-Tracking🐠 👉TIDE: tracking dense, homogeneous targets, providing a scalable dual-branch design t
🐠Dual-branch Elasticity ID-Tracking🐠 👉TIDE: tracking dense, homogeneous targets, providing a scalable dual-branch design to accommodate diverse hardware constraints. MIT license💙 👉Review https://t.ly/WEDeY 👉Paper https://arxiv.org/pdf/2607.26412 👉Project https://vranlee.github.io/TIDE/ 👉Repo https://github.com/vranlee/TIDE
4 280
5
🔥Decoder-only Any-to-Any Model🔥 👉MODUS unifies any-to-any multimodal generation with one decoder, two experts, and zero ta
🔥Decoder-only Any-to-Any Model🔥 👉MODUS unifies any-to-any multimodal generation with one decoder, two experts, and zero task heads. Impressive work. Repo under Apache💙 👉Review https://t.ly/-2QKT 👉Paper https://lnkd.in/dhfBQGhB 👉Project https://lnkd.in/dPD_ECXk 👉Repo https://lnkd.in/dbDHw24u
3 896
6
🍿 Dawn of Generative Cinematography 🍿 🟩 #TheOdyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost r
🍿 Dawn of Generative Cinematography 🍿 🟩 #TheOdyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture. 👉 Meanwhile, #AI research is heading in the exact opposite direction. 🟩 A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere. 👉More https://t.ly/Kd7RV 👉Paper arxiv.org/pdf/2607.24591 👉Project yixuanli98.github.io/cameraanything/ 👉Repo github.com/yixuanli98/CameraAnything
3 758
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🍿Dawn of Generative Cinematography🍿 🟩 The Odissey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture. 👉 Meanwhile, #AI research is heading in the exact opposite direction. 🟩 A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere. 🟩 Want a close-up? A drone shot? A ground-level perspective? A side angle? A cinematic tracking shot? You no longer decide where to place the camera during filming. You decide afterwards. 👉 And this fundamentally changes what cinematography means. 🟩 For more than a century, filmmakers have had to make irreversible decisions on set. Camera placement, focal length, movement, framing, etc. These choices became part of the recorded footage forever. 🟩 A scene becomes a 3D representation that can be "re-shot" endlessly from viewpoints that never physically existed. We simply capture "raw" data from which the final result is reconstructed or customized. 🟩Five years from now, will we still talk about shooting a movie? Or will we simply capture a scene and decide later where the camera should have been? The irony is fascinating. While Nolan reminds us how extraordinary a 70mm camera can be, AI is quietly suggesting that, soon, the camera itself will be optional. 👉The first step towards the generative cinematography. #deeplearning #computervision #AIwithPapers 👉Discussion https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2607.24591 👉Project yixuanli98.github.io/cameraanything/ 👉Repo github.com/yixuanli98/CameraAnything
1
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🍿🍿Dawn of Generative Cinematography🍿🍿 🟩#TheOdyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture. 👉Meanwhile, #AI research is heading in the exact opposite direction. 🟩A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere. 🟩Want a close-up? A drone shot? A ground-level perspective? A side angle? A cinematic tracking shot? You no longer decide where to place the camera during filming. You decide afterwards. 👉And this fundamentally changes what cinematography means. 🟩For more than a century, filmmakers have had to make irreversible decisions on set. Camera placement, focal length, movement, framing, etc. These choices became part of the recorded footage forever. 🟩A scene becomes a 3D representation that can be "re-shot" endlessly from viewpoints that never physically existed. We simply capture "raw" data from which the final result is reconstructed or customized. 🟩Five years from now, will we still talk about shooting a movie? Or will we simply capture a scene and decide later where the camera should have been? The irony is fascinating. While Nolan reminds us how extraordinary a 70mm camera can be, AI is quietly suggesting that, soon, the camera itself will be optional. 👉The first step towards the generative cinematography. #deeplearning #computervision #AIwithPapers 👉Discussion https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2607.24591 👉Project yixuanli98.github.io/cameraanything/ 👉Repo github.com/yixuanli98/CameraAnything
2
9
🔎MicroZoom at Extreme Scale🔎 👉MicroZoom by UWA synthesizes gigapixel-resolution images grounded in consumer-grade microsco
🔎MicroZoom at Extreme Scale🔎 👉MicroZoom by UWA synthesizes gigapixel-resolution images grounded in consumer-grade microscope close-ups at magnification levels up to 350×. Impressive. Repo under MIT💙 👉Review https://t.ly/hgJD7 👉Paper https://arxiv.org/pdf/2607.24729 👉Project https://microzoom-sr.github.io/ 👉Repo github.com/MicroZoom-SR/MicroZoom-SR.github.io
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💄MagicMakeup Makeup-Transfer💄 👉Makeup-transfer applies the reference makeup to the source face while preserving the source
💄MagicMakeup Makeup-Transfer💄 👉Makeup-transfer applies the reference makeup to the source face while preserving the source identity. Authors: Zhejiang University & vivo BlueImage Lab. Repo for non commercial💙 👉Review https://t.ly/JYpCr 👉Paper https://arxiv.org/pdf/2607.20924 👉Project https://vivocameraresearch.github.io/magicmakeup/ 👉Repo https://github.com/vivoCameraResearch/Magic-Makeup
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💢Unified Video Dense Prediction💢 👉UniD (Adobe Research + Cornell University) is a novel unified video model that jointly p
💢Unified Video Dense Prediction💢 👉UniD (Adobe Research + Cornell University) is a novel unified video model that jointly predicts: depth, surface normals, semantic segmentation, boundaries, human parts, albedo, shading, and materials. Code TBR💙 👉Review https://t.ly/oo7et 👉Paper https://arxiv.org/pdf/2607.21592 👉Project https://unid-video.github.io/ 👉Repo https://github.com/YihongSun/UniD
3 145
12
🫛Spatially-Aware Class-Agnostic Counting🫛 👉UpCount is reference-free, spatially aware, class-agnostic object counting with
🫛Spatially-Aware Class-Agnostic Counting🫛 👉UpCount is reference-free, spatially aware, class-agnostic object counting with an MAE-pretrained ViT, DPT-style feature reassembly, FeatUp-style joint bilateral upsampling, and proposal verification. Repo under MIT💙 👉Review https://t.ly/dWOc3 👉Paper https://arxiv.org/pdf/2607.16826 👉Repo github.com/r28112072-rgb/upcount
3 264
13
🦜Streaming 4D Transformer🦜 👉IGGT4D is a novel a streaming instance-grounded geometry transformer for online 4D scene under
🦜Streaming 4D Transformer🦜 👉IGGT4D is a novel a streaming instance-grounded geometry transformer for online 4D scene understanding. It processes video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. Repo/Data announced💙 👉Review https://t.ly/LFrKR 👉Paper https://arxiv.org/pdf/2607.19228 👉Project https://iggt4d.github.io/ 👉Repo TBA
3 123
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What about more posts about Robotics?
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👉Not a render. Not a concept. This is GENE.01 by Generative Bionics, the Italians coolest scaleup strikes back: in just six
👉Not a render. Not a concept. This is GENE.01 by Generative Bionics, the Italians coolest scaleup strikes back: in just six months, they turned GENE.01 into a fully functional humanoid platform that can walk, sense and interact. 👉Full-body multimodal skin perceives touch, proximity, force and temperature, bringing Physical AI closer to safe and natural collaboration with people. 👉More: https://t.ly/F3I3A
2 883
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🏯SOTA Music-to-Dance Gen🏯 👉The Tongyi Lab unveils Wan-Dancer, a novel stable minute-scale synthesis at 720p/30fps across f
🏯SOTA Music-to-Dance Gen🏯 👉The Tongyi Lab unveils Wan-Dancer, a novel stable minute-scale synthesis at 720p/30fps across five dance genres. Impressive results, new SOTA on long clip by a large margin. Repo under Apache 2.0💙 👉Review https://t.ly/AKY5j 👉Paper https://lnkd.in/d_xA7dwb 👉Project https://lnkd.in/dzfnw2h4 👉Repo https://lnkd.in/d-Zj_cTf
3 663
17
🌈FlowWAM: flow->action prediction🌈 👉FlowWAM is a novel dual-stream diffusion framework that adopts optical flow as a unifi
🌈FlowWAM: flow->action prediction🌈 👉FlowWAM is a novel dual-stream diffusion framework that adopts optical flow as a unified, video-native action representation. Repo under Apache💙 👉Review https://t.ly/FmutT 👉Paper https://arxiv.org/abs/2607.13017 👉Project https://flow-wam.github.io/ 👉Repo github.com/YixiangChen515/FlowWAM
3 930
18
🦧 MonkeyOCRv2 is out! 🦧 👉MonkeyOCRv2 is a text-centric visual foundation model that unifies fine-grained text modeling, cr
🦧 MonkeyOCRv2 is out! 🦧 👉MonkeyOCRv2 is a text-centric visual foundation model that unifies fine-grained text modeling, cross-task representation learning, and cross-lingual generalization in a single encoder. Released for academic research and non-commercial use💙 👉Review https://t.ly/yicEK 👉Paper https://arxiv.org/pdf/2607.11562 👉Repo https://github.com/Yuliang-Liu/MonkeyOCRv2
3 887
19
🎂REMIND: long-term MOT re-ID🎂 👉REMIND by CVAR-UPM is a novel online tracker designed for long-term multi-object re-ID of g
🎂REMIND: long-term MOT re-ID🎂 👉REMIND by CVAR-UPM is a novel online tracker designed for long-term multi-object re-ID of generic indoor objects from monocular RGB, requiring neither camera pose nor depth. Repo under MIT💙 👉Review https://t.ly/AkQoI 👉Paper https://lnkd.in/dm58mkCv 👉Project https://lnkd.in/dZrAZqFe 👉Repo https://lnkd.in/dbidrwxU
3 589
20
🌔Foundation Global SFM🌔 👉Glob3R is a global SfM-style reconstruction built on 3D foundation models. key idea: explicitly o
🌔Foundation Global SFM🌔 👉Glob3R is a global SfM-style reconstruction built on 3D foundation models. key idea: explicitly optimize feed-forward geometric predictions. Repo TBA💙 👉Review https://t.ly/Z_4C7 👉Paper https://arxiv.org/pdf/2607.09225 👉Project https://junyuandeng.github.io/Glob3r/ 👉Repo TBA
3 509