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 17 262 مشتركاً، محتلاً المرتبة 7 491 في فئة التكنولوجيات والتطبيقات والمرتبة 2 128 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 262 مشتركاً.
بحسب آخر البيانات بتاريخ 23 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 255، وفي آخر 24 ساعة بمقدار 2، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 18.93%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 7.17% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 3 267 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 237 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 20.
- 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 24 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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+224 ساعات
+67 أيام
+25530 أيام
أرشيف المشاركات
💥Geo4D: VideoGen 4D Scene💥
👉The Oxford VGG unveils Geo4D: video diffusion for monocular 4D reconstruction. Only synthetic data for training, but strong generalization to real world: point maps, depth & ray maps for the new SOTA in dynamic reconstruction. Code released💙
👉Review https://t.ly/X55Uj
👉Paper arxiv.org/pdf/2504.07961
👉Project geo4d.github.io/
👉Code github.com/jzr99/Geo4D
🥊 Pose in Combat Sports 🥊
👉The novel SOTA framework for an accurate physics-based #3D human pose estimation in combat sports w/ sparse multi-cameras setup. Dataset to be released soon💙
👉Review https://t.ly/EfcGL
👉Paper https://lnkd.in/deMMrKcA
👉Project https://lnkd.in/dkMS_UrH
🧊BoxDreamer Object Pose🧊
👉BoxDreamer is a generalizable RGB-based approach for #3D object pose estimation in the wild, specifically designed to address challenges in sparse-view settings. Code coming, demo released💙
👉Review https://t.ly/e-vX9
👉Paper arxiv.org/pdf/2504.07955
👉Project https://lnkd.in/djz8jqn9
👉Repo https://lnkd.in/dfuEawSA
🤗Demo https://lnkd.in/dVYaWGcS
💛 Unified Scalable SVG Generator 💛
👉OmniSVG is the first family of e2e multimodal generators that leverages pre-trained VLMs to create detailed SVGs. Code, models & dataset to be released under MIT💙
👉Review https://t.ly/JcR3I
👉Paper https://arxiv.org/pdf/2504.06263
👉Project https://omnisvg.github.io/
👉Repo github.com/OmniSVG/OmniSVG
👉Dataset https://huggingface.co/OmniSVG
🐈 TTT Long Video Generation🐈
👉A novel architecture for video generation adapting the CogVideoX 5B model by incorporating Test-Time Training layers. Adding TTT layers into a pre-trained Transformer -> one-minute clip from text storyboards. Videos, code & annotations released💙
👉Review https://t.ly/mhlTN
👉Paper arxiv.org/pdf/2504.05298
👉Project test-time-training.github.io/video-dit/
👉Repo github.com/test-time-training/ttt-video-dit
⛽ VoRA: Vision as LoRA ⛽
👉#ByteDance unveils Vision as LoRA (VoRA), a novel paradigm converting LLMs into Multimodal Large Language Models (MLLMs) by integrating vision-specific LoRA layers. All training data, codes, and model weights available💙
👉Review https://t.ly/guNVN
👉Paper arxiv.org/pdf/2503.20680
👉Repo github.com/Hon-Wong/VoRA
👉Project georgeluimmortal.github.io/vora-homepage.github.io/
🌳 Compose Anything is out 🌳
👉Skywork AI unveils SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized videos based on textual prompts. Code, models, & evaluation benchmark released💙
👉Review https://t.ly/MEjzL
👉Paper https://arxiv.org/pdf/2504.02436
👉Project skyworkai.github.io/skyreels-a2.github.io/
👉Repo github.com/SkyworkAI/SkyReels-A2
🤗Models https://huggingface.co/Skywork/SkyReels-A2
💃 Video Motion Graphs 💃
👉#Adobe unveils a novel system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the system synthesizes amazing new videos. Code & Models to be released💙
👉Review https://t.ly/r4EGF
👉Paper https://lnkd.in/dK_tHyzh
👉Project https://lnkd.in/dE6c_KYZ
👉Repo TBA
🐟Segment Any Motion in Video🐟
👉From CVPR2025 a novel approach for moving object segmentation that combines DINO-based semantic features and SAM2. Code under MIT license💙
👉Review https://t.ly/4aYjJ
👉Paper arxiv.org/pdf/2503.22268
👉Project motion-seg.github.io/
👉Repo github.com/nnanhuang/SegAnyMo
🌳MSVA Zero-Shot Multi-View🌳
👉Niantic unveils MVSA, novel Multi-View Stereo Architecture to work anywhere by generalizing across diverse domains & depth ranges. Highly accurate & 3D-consistent depths. Code & models announced💙
👉Review https://t.ly/LvuTh
👉Paper https://arxiv.org/pdf/2503.22430
👉Project https://nianticlabs.github.io/mvsanywhere/
👉Repo https://lnkd.in/ddQz9eps
🏓LATTE-MV: #3D Table Tennis🏓
👉UC Berkeley unveils at #CVPR2025 a novel system for reconstructing monocular video of table tennis in 3D with uncertainty-aware controller that anticipates opponent actions. Code & Dataset announced, to be released💙
👉Review https://t.ly/qPMOU
👉Paper arxiv.org/pdf/2503.20936
👉Project sastry-group.github.io/LATTE-MV/
👉Repo github.com/sastry-group/LATTE-MV
🦎 Scaling Vision to 4K🦎
👉PS3 by #Nvidia (+UC Berkeley) to scale-up CLIP-style vision pre-training to 4K with *near-constant* cost. Encoding LR global image and selectively processes only informative HR regions. Impressive work. Code/weights & 🤗 announced💙
👉Review https://t.ly/WN479
👉Paper https://lnkd.in/ddWq8UpX
👉Project https://lnkd.in/dMkTY8-k
👉Repo https://lnkd.in/d9YSB6yv
🔥 Dereflection Any Image 🔥
👉SJTU & #Huawei unveils DAI, novel diffusion-based framework able to recover from a wide range of reflection types. One-step diffusion with deterministic outputs & fast inference. Inference, pretrained models & training released💙
👉Review https://t.ly/PDA9K
👉Paper https://arxiv.org/pdf/2503.17347
👉Project abuuu122.github.io/DAI.github.io/
👉Repo github.com/Abuuu122/Dereflection-Any-Image
🙀3D MultiModal Memory🙀
👉M3 is a novel framework by UCSD & #NVIDIA for rendering 3D scenes w/ RGB & foundation model embeddings. Rich spatial & semantic understanding via novel memory system designed to retain multimodal info through videos
👉Review https://t.ly/OrXZO
👉Paper arxiv.org/pdf/2503.16413
👉Project https://lnkd.in/dXAZ97KH
👉Repo https://lnkd.in/dWvunCET
🥎LLM Spatial Understanding🥎
👉SpatialLM by Manycore: novel LLM designed to process 3D point cloud data and generate structured 3D scene understanding outputs. Code, model & data 💙
👉Review https://t.ly/ejr1s
👉Project manycore-research.github.io/SpatialLM/
👉Code github.com/manycore-research/SpatialLM
🤗Models https://huggingface.co/manycore-research
teaser (1) (online-video-cutter.com) (1).mp42.02 MB
🧞 IMPOSSIBLE Videos 🧞
👉IPV-Bench: counterfactual and anti-reality scenes impossible in real world. A novel challenge designed to evaluate and foster progress in video understanding and generation. Code & 🤗-Data 💙
👉Review https://t.ly/D7jhm
👉Paper arxiv.org/pdf/2503.14378
👉Project showlab.github.io/Impossible-Videos/
👉Repo github.com/showlab/Impossible-Videos
🌱 #Py4AI: line-up is official 🌱
👉Last week we announced the first part of our incredible line-up for PY4AI 2025. It's time to disclose the second one and drive you crazy👇
𝐓𝐡𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 𝐛𝐚𝐭𝐜𝐡 𝐨𝐟 𝐬𝐩𝐞𝐚𝐤𝐞𝐫𝐬:
🔥Alfredo Canziani | New York University
🔥Fanny Bouton | OVHcloud
🔥Full list: https://t.ly/JJP8B
🧸 Occluded 3D Reconstruction 🧸
👉Oxford unveils a novel 3D generative model to reconstruct 3D objects from partial observations. Code (TBR), demo, model on HF💙
👉Review https://t.ly/Lr5D7
👉Paper arxiv.org/pdf/2503.13439
👉Project sm0kywu.github.io/Amodal3R/
🤗huggingface.co/spaces/Sm0kyWu/Amodal3R
