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 7 509 في فئة التكنولوجيات والتطبيقات والمرتبة 2 130 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
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 16.91%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 7.04% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 918 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 26 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
17 261
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+224 ساعات
+17 أيام
+26130 أيام
أرشيف المشاركات
🧤 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/
🪰 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
🦓 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
⛈️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
🔥 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/
🍀 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/
👚 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
🖼️ Diffusive Images that Sound 🖼️
👉The University of Michigan unveils a diffusion model able to generate spectrograms that look like images but can also be played as sound.
👉Review https://t.ly/ADtYM
👉Paper arxiv.org/pdf/2405.12221
👉Project ificl.github.io/images-that-sound
👉Code github.com/IFICL/images-that-sound
⚽ 3D Shot Posture from Broadcast ⚽
👉Nagoya Univeristy unveils 3DSP soccer broadcast videos, the most extensive sports image dataset with 2D pose annotations EVER. Data and Source Code released💙
👉Review https://t.ly/IIMeZ
👉Paper https://arxiv.org/pdf/2405.12070
👉Code https://github.com/calvinyeungck/3D-Shot-Posture-Dataset/tree/master
🦕 Grounding DINO 1.5 Pro/Edge 🦕
👉Grounding DINO 1.5, a suite of advanced open-set object detection models to advanced the "Edge" of open-set object detection. Source Code released under Apache 2.0💙
👉Review https://t.ly/kS-og
👉Paper https://lnkd.in/dNakMge2
👉Code https://lnkd.in/djhnQmrm
🫀 EchoTracker: Tracking Echocardiography🫀
👉NTNU unveils EchoTracker, a two-fold coarse-to-fine model that facilitates the tracking of queried points on a tissue surface across ultrasound. Source Code released💙
👉Review https://t.ly/NyBe0
👉Paper https://arxiv.org/pdf/2405.08587
👉Code https://github.com/riponazad/echotracker/
🔥EfficientTrain++: Efficient Foundation Visual Backbone Training🔥
👉Tsinghua unveils EfficientTrain++, a simple, general, surprisingly effective, off-the-shelf approach to reduce the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer). Up to 3.0× faster on ImageNet-1K/22K without sacrificing accuracy. Source Code released 💙
👉Review https://t.ly/D8ttv
👉Paper https://arxiv.org/pdf/2405.08768
👉Code https://github.com/LeapLabTHU/EfficientTrain
🪬UHM: Authentic Hand by Phone🪬
👉 META unveils UHM, novel 3D high-fidelity avatarization of your (yes, the your one) hand. Adaptation pipeline fits the pre-trained UHM via phone scan. Source Code released 💙
👉Review https://t.ly/fU5rA
👉Paper https://lnkd.in/dyGaiAnq
👉Code https://lnkd.in/d9B_XFAA
👻 3D Humans Motion from Text 👻
👉Zhejiang (+ANT) unveils a novel method to generate human motions containing accurate human-object interactions in 3D scenes based on textural descriptions. Code announced, coming 💙
👉Review https://t.ly/eOZnU
👉Paper https://arxiv.org/pdf/2405.07784
👉Project https://zju3dv.github.io/text_scene_motion/
🐏 AniTalker: Universal Self-Talking Humans 🐏
👉SJTU (+AISpeech) unveils AniTalker, a framework that transforms a single static portrait and input audio into animated talking videos with naturally flowing movements.
👉Review https://t.ly/MD4yX
👉Paper https://arxiv.org/pdf/2405.03121
👉Project https://x-lance.github.io/AniTalker/
👉Repo https://github.com/X-LANCE/AniTalker
💥FeatUp: Model at Any Resolution💥
👉FeatUp is a task- and model-agnostic framework to restore lost spatial information in deep features. It outperforms other feature upsampling in class activation map generation, transfer learning for segmentation and depth prediction, and end-to-end training for semantic segm. Source Code released💙
👉Review https://t.ly/Evq_g
👉Paper https://lnkd.in/gweaN4s6
👉Project https://lnkd.in/gWcGXdxt
👉Code https://lnkd.in/gweq5NY4
🔫 Free-Moving Reconstruction 🔫
👉EPFL (+#MagicLeap) unveils a novel approach for reconstructing free-moving object from monocular RGB clip. Free interaction with objects in front of a moving cam without relying on any prior, and optimizes the sequence globally without any segments. Great but no code announced🥺
👉Review https://t.ly/2xhtj
👉Paper arxiv.org/pdf/2405.05858
👉Project haixinshi.github.io/fmov/
🦑 Hyper-Detailed Image Descriptions 🦑
👉#Google unveils ImageInWords (IIW), a carefully designed HIL annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process
👉Review https://t.ly/engkl
👉Paper https://arxiv.org/pdf/2405.02793
👉Repo https://github.com/google/imageinwords
👉Project https://google.github.io/imageinwords
👉Dataset huggingface.co/datasets/google/imageinwords
👉Explorer huggingface.co/spaces/google/imageinwords-explorer
🍏 XFeat: Neural Features Matching 🍏
👉XFeat (Accelerated Features) is lightweight/accurate architecture for efficient visual correspondence. It revisits fundamental design choices in CNN for detecting, extracting & matching local features
👉Review https://t.ly/ppb38
👉Paper arxiv.org/pdf/2404.19174
👉Code https://lnkd.in/dFzTpzN8
👉Project https://lnkd.in/d8JnV-iu
🏷️DiffMOT (#CVPR24): diffusion-MOT🏷️
👉DiffMOT is a novel real-time diffusion-based MOT approach to tackle the complex nonlinear motion. Impressive results & Source Code released💙
👉Review https://t.ly/ztlHi
👉Paper https://lnkd.in/d4K3c-nt
👉Project https://diffmot.github.io/
👉Code github.com/Kroery/DiffMOT
