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 17 261 مشتركاً، محتلاً المرتبة 7 543 في فئة التكنولوجيات والتطبيقات والمرتبة 2 144 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 261 مشتركاً.
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- حالة التحقق: غير موثّقة
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- وصول المنشورات: يحصل كل منشور على متوسط 3 008 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 216 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 16.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل framework, object, dataset, tba, depth.
📝 الوصف وسياسة المحتوى
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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 27 أكتوبر, 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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أرشيف المشاركات
☔ #SelfDriving? It's all about weather! ☔
👉Novel self-supervised MDE method to handle adverse weather in real-world autonomous driving
😎Review https://t.ly/tcLQW
😎Paper arxiv.org/pdf/2307.08357.pdf
😎Project kieran514.github.io/Robust-Depth-Project/
🐈 Gen-AI as representation learner 🐈
👉DreamTeacher: novel self-supervised feats. representation learning framework that utilizes gen-nets for pre-training downstream image backbones
😎Review https://t.ly/RL8iG
😎Paper arxiv.org/pdf/2307.07487.pdf
😎Project research.nvidia.com/labs/toronto-ai/DreamTeacher
🧯 Neural Focal Modulation for VAR 🧯
👉Video-FocalNet is a novel architecture for video recognition that models both local and global context
😎Review https://t.ly/rF_fk
😎Paper arxiv.org/pdf/2307.06947.pdf
😎Project talalwasim.github.io/Video-FocalNets
😎Code github.com/TalalWasim/Video-FocalNets
💡DATID-3D: Diffusive Text-to-3D Generation💡
👉 A novel domain adaptation method for 3D via text-to-image diffusion. 🤗-Demo available!
😎Review https://t.ly/ecBvM
😎Paper arxiv.org/pdf/2211.16374.pdf
😎Project gwang-kim.github.io/datid_3d/
😎Code github.com/gwang-kim/DATID-3D
🤗Demo huggingface.co/spaces/gwang-kim/DATID-3D
😎Colab colab.research.google.com/drive/1e9NSVB7x_hjz-nr4K0jO4rfTXILnNGtA?usp=sharing
🎪 Extreme Human Pose Estimation 🎪
👉RePoGen: novel synthetic data generator of extreme/realistic poses of humans
😎Review https://t.ly/ecBvM
😎Paper arxiv.org/pdf/2307.06737.pdf
😎Project mirapurkrabek.github.io/RePoGen-paper
😎Code github.com/MiraPurkrabek/RePoGen
🃏 Deepfake via casual self-scan 🃏
👉TAU presents a novel approach to reenact an ID using only a casual self-scan
😎Review https://t.ly/9T8Wi
😎Paper arxiv.org/pdf/2307.06307.pdf
😎Project arielazary.github.io/PGR
🔥o-TTT: Test-Time Training on fire 🔥
👉Extending the TTT to the streaming setting. Suitable for Panoptic, Instance & Colorization.
😎Review https://t.ly/eZYA
😎Paper arxiv.org/pdf/2307.05014.pdf
😎Project https://video-ttt.github.io/
😎Code github.com/renwang435/video-ttt-release
🍡 Text2Cinemagraphs: Cinemagraph from text 🍡
👉CMU (+ #Snap) unveils a fully automated method for creating cinemagraphs from text descriptions
😎Review https://t.ly/BwZs6
😎Paper arxiv.org/pdf/2307.03190.pdf
😎Project text2cinemagraph.github.io/website/
😎Code github.com/text2cinemagraph/text2cinemagraph
🛣️ STAR.: 3D-tracking w/ attention paradigm 🛣️
👉#Mercedes STAR: e2e 3D object tracking that follows the tracking-by-attention paradigm
😎Review https://t.ly/JoGj
😎Paper arxiv.org/pdf/2306.17602.pdf
😎Project simondoll.github.io/publications/star_track
🪩 DISCO: Human Dance Generation 🪩
👉NTU (+ #Microsoft) unveils DISCO: a big step towards the Human Dance Generation.
😎Review https://t.ly/cNGX
😎Paper arxiv.org/pdf/2307.00040.pdf
😎Project disco-dance.github.io/
😎Code github.com/Wangt-CN/DisCo
🔮 SAM-PT: Segment Anything + Tracking 🔮
👉SAM-PT is the first method to utilize sparse point propagation for Video Object Segmentation (VOS).
😎Review https://t.ly/QLMG
😎Paper arxiv.org/pdf/2307.01197.pdf
😎Project www.vis.xyz/pub/sam-pt/
😎Code github.com/SysCV/sam-pt
🔥🔥 Source Code IS OUT! 🔥🔥
More: https://t.ly/ZddLl
🍥 PanoHead: 3D Full-Head Synthesis 🍥
👉#ByteDance (+UW-M) unveils PanoHead: 360◦ view-consistent portraits from a single-view image
😎Review https://t.ly/MrLNR
😎Paper arxiv.org/pdf/2303.13071.pdf
😎Project sizhean.github.io/panohead
😎Code github.com/sizhean/panohead
🚔 Fooling Neural Forensic Classifiers 🚔
👉Adversarial faces able to fool the forensic classifiers, while remaining undetectable by humans
😎Review https://t.ly/33Cc
😎Paper arxiv.org/pdf/2306.13091.pdf
😎Project koushiksrivats.github.io/face_attribute_attack
😎Code github.com/koushiksrivats/face_attribute_attack
🦷 Few-Shot Geometry-Aware Keypoints 🦷
👉UBC (+Flawless AI) unveils the new SOTA in semantic keypoints localization. Suitable for faces, animals, cars, mouth, teeth & more
😎Review https://t.ly/-0qN
😎Paper arxiv.org/pdf/2303.17216.pdf
😎Project xingzhehe.github.io/FewShot3DKP/
🫣 Text-Guided Adversarial Makeup 🫣
👉Novel facial privacy protection via adversarial latent codes. Makeup vs Face Recognition.
😎Review https://t.ly/pBCP
😎Paper arxiv.org/pdf/2306.10008.pdf
😎Code github.com/fahadshamshad/Clip2Protect
🧿 NeRF-Supervised Deep Stereo 🧿
👉A novel pioneering pipeline for training deep stereo networks WITH NO ground-truth
😎Review https://t.ly/c7j-
😎Project nerfstereo.github.io/
😎Dataset https://amsacta.unibo.it/id/eprint/7218/
😎Code github.com/fabiotosi92/NeRF-Supervised-Deep-Stereo
😎Paper https://openaccess.thecvf.com/content/CVPR2023/papers/Tosi_NeRF-Supervised_Deep_Stereo_CVPR_2023_paper.pdf
👁️ Scene Five: Through Her Eyes 👁️
👉 #3D scene reconstruction of what a person is observing using only the reflections of their eyes
😎Review https://t.ly/Krvw
😎Paper arxiv.org/pdf/2306.09348.pdf
😎Project https://world-from-eyes.github.io/
🌈 Track Everything Everywhere 🌈
👉#Google unveils OmniMotion: full-length motion tracking for every pixel in every frame of video.
😎Review https://t.ly/Krvw
😎Paper arxiv.org/pdf/2306.05422.pdf
😎Project omnimotion.github.io/
😎Demo omnimotion.github.io/#interactive_demo
😎Code github.com/qianqianwang68/omnimotion
🏸 Segment Anything in HQ 🏸
👉HQ-SAM: SAM with the ability to accurately segment objects, maintaining promptable design, efficiency, zero-shot generalizability
😎Report https://t.ly/GxX5B
😎Paper arxiv.org/pdf/2306.01567.pdf
😎Models github.com/SysCV/SAM-HQ
