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 17 144 مشترک است و جایگاه 7 701 را در دسته فناوری و برنامهها و رتبه 2 225 را در منطقه ماليزيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 144 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 25 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -186 و در ۲۴ ساعت گذشته برابر 3 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 23.94% 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 0 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 0 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 26 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.
17 144
مشترکین
+324 ساعت
-367 روز
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آرشیو پست ها
🍋YOLOPv2: Better Driving Perception🍋
👉YOLOPv2: simultaneous object, road segmentation & lane detection
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅E2E perception net with better backbone
✅Efficient ELAN for reasonable memory
✅Stability for adapting to scenarios
✅SOTA on BDD100K, +50% faster!
✅Source code under MIT license
More: https://bit.ly/3LvYGBh
🐸 CHARL-E: Stable Diffusion in 1 click 🐸
👉CHARL-E packages Stable Diffusion into a simple app.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅No setup, dependencies, or internet
✅Images with 1-click on #macbook
✅Suitable only for M1/M2 processor
✅Source code under MIT license
More: https://bit.ly/3xv2z3G
🍐PeRFception: Largest IR Dataset🍐
👉#Nvidia, a new frontier in data collection via Plenoxels: same info, -96.4% in size.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅POSTECH + NVIDIA + Caltech = 🤯
✅Size: -96.4% from original dataset!
✅2D/3D image/object class/semantic
✅Ready-to-use pipeline for implicit dataset
More: https://bit.ly/3eW9hJA
🟨 Lang<->Pics in 100+ Languages 🟨
👉#Google PaLI: unified lang-image #AI to perform tasks in 109 languages 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅PaLI: Pathways Lang & Image model
✅Answering, captioning, reasoning, etc
✅From Eng. to 109 lang. understanding
✅The new SOTA on several datasets
More: https://bit.ly/3QMslHC
🈯SAMURAI: in-the-wild Shape/Material🈯
👉#Google SAMURAI: shape, BRDF, per-image pose & illumination. Relightable #3D assets for #AR/#VR.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Parametrization for varying distances
✅Camera multiplex optimization
✅Posterior scaling of input images
✅Explicit meshes extraction with BRDF
✅Code/data soon available ->#NeurIPS
More: https://bit.ly/3BKWgf3
🉐#AI finds where IG photos are taken🉐
👉Brilliant work of Depoorter, Belgium artist that handles #privacy, #AI & #socialmedia
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Recorded open cameras for weeks
✅Scraped all #Instagram photos
✅Matching Instagram vs. footage
More: https://bit.ly/3eL5dfc
🔥🔥 UPDATE 🔥🔥
Code Released: https://github.com/sczhou/CodeFormer
🔥 A Survey on Diffusion Models 🔥
👉A comprehensive review of denoising diffusion models in #computervision 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Overview on diffusion models
✅Hot trend for the generative AI
✅A multi-perspective categorization
✅Current limitations / new directions
More: https://bit.ly/3RYG5zP
💮MAXIM: Multi-Axis MLP for Vision💮
👉#Google opens MAXIM, a multi-axis MLP for low-level vision
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Denoising, deblurring, dehazing, etc
✅Multi-axis gated MLP, linear complexity
✅Cross gating block, separate features
✅SOTA results on several datasets!
More: https://bit.ly/3Dmp8LI
🏵️ TORAS: SOTA #AI for annotation 🏵️
👉TORAS: web-based AI-powered, cooperative, annotation platform.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SOTA AI tools -> significant speedup
✅"Recipes" to define how to annotate
✅Repo with folder structure for storage
✅Also on-prem for (commercial) firms
More: https://bit.ly/3L78YI2
💜 #Selfdriving in 80's. Damn Romantic 💜
👉The first self-driving car with people on board, 1986. So slow and lovely.
More: https://bit.ly/3BtRDon
🥤K-VIL: Keypoint-based visual imitation🥤
👉K-VIL: auto-incremental extraction of object-centric task representation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Efficient task-relevant keypoints
✅Embodiment-independent tasks
✅Adaptation of tasks to new scenes
✅Input: only a small set of demo clips
✅Novel keypoint-based controller
More: https://bit.ly/3eIrxpP
🐲 Open-Source Self-Driving projects 🐲
👉A free repo with many autonomous vehicle-related projects
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Basic/Advance Lane/Line Detection
✅Driving behavior by training & validating
✅Autopilot: predicting steering angle
More: https://bit.ly/3qqJ7RB
🎪 SOTA in Arbitrary Shape Text Detection 🎪
👉Novel unified coarse-to-fine Transformer for arbitrary shape text detection
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Coarse-to-fine arbitrary text detection
✅Accurate text detection, NO post-process
✅Boundary proposal generation mechanism
✅Innovative boundary transformer (iterative)
✅Boundary energy loss (BEL) for refinement
More: https://bit.ly/3D6Ryt4
👹TT-GNeRF: generative NeRF for Faces👹
👉TT-GNeRF: a novel 3D-aware GANs based on generative NeRF for faces
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ETH + Uni_Trento + #Snap 🤯
✅DAEM for disentanglement of 3D model
✅"Training-as-Init, Optimizing-for-Tuning"
✅Consistency++, preserving non-target ROI
✅Unsupervised optimization of geometry
More: https://bit.ly/3ARZmMw
🌈 X-NeRF: Cross-Spectral NeRF 🌈
👉Cross-Spectral NeRF from cams with different light spectrums
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅First ever cross-spectral NeRF
✅Avoiding non-trivial calib/match
✅Normalized Cross-Device Coords
✅Novel dataset w/ RGB, MS, & IR
More: https://bit.ly/3RqHnUo
🐠VIS - Deformable Transformers 🐠
👉DeVIS: VIS method with efficiency and performance of deformable ViT
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Temp. multi-scale D-Attention
✅Instance-aware object queries
✅Mask: DA + multi-scale feats map
✅Improved multi-cue clip tracking
✅SOTA on YouTube-VIS 2021/OVIS
More: https://bit.ly/3TQv1Xc
🤯 #StableDiffusion + #Dallemini = BOOM! 🤯
👉A #colab notebook that combines Stable Diffusion + DALL-E Mini (Craiyon)
More: https://bit.ly/3TTOshR
🦎 VMT: Video Mask Transfiner 🦎
👉Novel highly efficient ViT structure for video instance segmentation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅HD & more temporally stable mask
✅Higher resolution features for VIS
✅Detecting error-prone s-t. regions
✅Auto-refinement on training data!
More: https://bit.ly/3RKXtb4
🫐 Stable Diffusion Video is out! 🫐
👉A free notebook to generate videos by interpolating the latent space of SD.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Blueberry to strawberry spaghetti
✅Dream items from same prompt
✅Morph different prompts (seeds)
✅Built on a script by A. Karpathy
More: https://bit.ly/3ey8632
