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.

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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

المشتركون

-624 ساعات

-277 أيام

-16630 أيام

4 057

عرض المشاهدات

~ 1 17724 ساعات

~ 1 35348 ساعات

23.63%

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17 151

🖼️ 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

17 151

⚽ 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

17 151

🦕 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

17 151

🫀 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/

17 151

🔥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

17 151

🪬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

17 151

👻 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/

17 151

🐏 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

17 151

💥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

17 151

🔫 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/

17 151

🦑 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

17 151

🍏 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

17 151

🏷️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

17 151

🌊 Diffusive 3D Human Recovery 🌊 👉The Rutgers University unveils ScoreHMR at #CVPR24; novel approach for 3D human pose and shape reconstruction. Impressive results. 👉Review https://t.ly/G0k2D 👉Paper https://arxiv.org/pdf/2403.09623 👉Code https://github.com/statho/ScoreHMR 👉Project https://statho.github.io/ScoreHMR/

17 151

🌐 3D Scenes with Depth Inpainting 🌐 👉Oxford announced two novel contributions to the field of 3D scene generation: a new benchmark and a novel depth completion model. 🤗-Demo and Source Code released💙 👉Review https://t.ly/BKiny 👉Paper https://arxiv.org/pdf/2404.19758 👉Project https://research.paulengstler.com/invisible-stitch/ 👉Code https://github.com/paulengstler/invisible-stitch 👉Demo https://huggingface.co/spaces/paulengstler/invisible-stitch

17 151

🏝️1000x Scalable Neural 3D Fields🏝️ 👉Highly-scalable neural 3D Fields: 1000x reductions in memory maintaining speed/quality: 10 MB vs. 10 GB! Code released 💙 👉Review https://t.ly/sLTK5 👉Paper https://lnkd.in/dEYM8-t2 👉Project https://lnkd.in/djptdujx 👉Code https://lnkd.in/dcCnFZ2n

17 151

🪷 Tunnel Try-on: SOTA VTON 🪷 👉"Tunnel Try-on", the first diffusion-based video virtual try-on model that demonstrates SOTA performance in complex scenarios. No code announced :( 👉Review https://t.ly/joMtJ 👉Paper arxiv.org/pdf/2404.17571 👉Project mengtingchen.github.io/tunnel-try-on-page/

17 151

👗TELA: Text to 3D Clothed Human👗 👉 TELA is a novel approach for the new task of clothing disentangled 3D human model generation from texts. This novel approach unleashes the potential of many downstream applications (e.g., virtual try-on). 👉Review https://t.ly/6N7JV 👉Paper https://arxiv.org/pdf/2404.16748 👉Project https://jtdong.com/tela_layer/ 👉Code https://github.com/DongJT1996/TELA

17 151

🌊 FlowMap: dense depth video 🌊 👉MIT (+CSAIL) unveils FlowMap, a novel E2E differentiable method that solves for precise camera poses, camera intrinsics, and perframe dense depth of a video sequence. Source Code released 💙 👉Review https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2404.15259.pdf 👉Project cameronosmith.github.io/flowmap 👉Code github.com/dcharatan/flowmap

17 151

🎡 NER-Net: Seeing at Nighttime 🎡 👉Huazhong (+Beijing) unveils a novel event-based nighttime imaging solution under non-uniform illumination, plus a paired multi-illumination level real-world dataset. Repo online, code coming 💙 👉Review https://t.ly/Z9JMJ 👉Paper arxiv.org/pdf/2404.11884.pdf 👉Repo github.com/Liu-haoyue/NER-Net 👉Clip https://www.youtube.com/watch?v=zpfTLCF1Kw4