اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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
نمایش بیشتر📈 تحلیل کانال تلگرام AI with Papers - Artificial Intelligence & Deep Learning
کانال AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 17 154 مشترک است و جایگاه 7 726 را در دسته فناوری و برنامهها و رتبه 2 240 را در منطقه ماليزيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 154 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 21 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -166 و در ۲۴ ساعت گذشته برابر -6 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 23.63% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 6.86% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 4 057 بازدید دریافت میکند. در اولین روز معمولاً 1 177 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 26 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند framework, object, dataset, tba, depth تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 22 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
17 154
مشترکین
-624 ساعت
-277 روز
-16630 روز
آرشیو پست ها
🖼️ 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
🌊 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/
🌐 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
🏝️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
🪷 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/
👗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
🌊 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
🎡 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
