AI with Papers - Artificial Intelligence & Deep Learning
رفتن به کانال در Telegram
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 17 264 مشترک است و جایگاه 7 532 را در دسته فناوری و برنامه‌ها و رتبه 2 128 را در منطقه ماليزيا دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 264 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 30 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 223 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 16.65% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.01% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 2 874 بازدید دریافت می‌کند. در اولین روز معمولاً 1 211 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 16 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 31 اکتبر, 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.
17 264
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آرشیو پست ها
🔥🔥 UPDATE 🔥🔥
Code Released: https://github.com/apple/ml-mobileone
🧱 Assembling #LEGO with #AI 🧱
👉Step-by-step assembly manual created by human into machine-interpretable instructions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Stanford + MIT + #Google 🤯
✅MEPNet: Manual-to-Executable-Plan Net
✅Manual to machine-executable plan
✅2D manual - 3D geometric shape
✅Reasoning on 3D alignments of legos
More: https://bit.ly/3PCwn5C
💄DEVIANT: SOTA in mono-3D detection💄
👉A novel Depth EquiVarIAnt NeTwork for 3D monocular detection in the wild
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Michigan + #Meta + Ford 🤯
✅Depth-equi. + scale equiv. steerable
✅New SOTA on KITTI & Waymo
✅Ok cross-dataset -> generalization
More: https://bit.ly/3OEFtgK
👹Multiface Neural Rendering 👹
👉A new multi-view, Hi-Res data collected at #META Reality Labs for neural face
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Mugsy, large scale multi-cam apparatus
✅High-Res sync facial performance
✅Closing the gap in accessing HQ data
✅Suitable for #VR & #mixedreality
More: https://bit.ly/3b6XfeL
🎷🎷OMNI3D: #3D Objects in the Wild🎷🎷
👉#3D detection: 234k images, 3M+ instances & 97 categories
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅OMNI3D from publicly released dataset
✅234k pics, 3M+ annotation with 3D box
✅97 categories such as sofa, table, cars
✅Fast (450x) and exact algorithm for IoU
✅Cube R-CNN: novel 3D object detector
More: https://bit.ly/3cznjzG
🔥 #AIwithPapers: we are 3,500+! 🔥
💙💛 Ready for YOLO 10, 11, π, ∞, Ψ, and more? The more we are, the faster we catch'em all 💙💛
😈 Invite your friends -> https://t.me/AI_DeepLearning
🪰NUWA-Infinity is out!🪰
👉∞ generation by #Microsoft: arbitrarily-sized HD images and long videos 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unconditional Image Gen.
✅Text-to-Image/Text-to-Clip
✅Animation / Out-painting
✅Hi-res, arbitrary long clip
✅NCP for patches caching
More: https://bit.ly/3zmBf9f
🦚 TimeLens++: Event-based Interpolation 🦚
👉Novel event-based interpolation with non-linear flow & multi-scale fusion
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel motion spline estimator
✅Non-linear continuous event/frames flow
✅Multi-feature fusion, gated compression
✅Novel hybrid dataset with 100+ videos
More: https://bit.ly/3yJyY6g
🔥Important update from #OpenAI🔥
👉 https://openai.com/blog/reducing-bias-and-improving-safety-in-dall-e-2/
💣 HD Neural Avatar @130FPS 💣
👉Samsung unveils MegaPortraits: novel one-shot creation of HD neural human avatar
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅One-shot neural avatars, SOTA up 512p
✅"Upgrading" to megapixel via more pics
✅First Neural Head Avatars in HD
✅Up to to 130 FPS via #GPU
More: https://bit.ly/3oboWWT
🔥OmniBenchmark: CV beyond ImageNet🔥
👉 21 realms, 7,000+ concepts and 1M+ images. Far beyond ImageNet!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅vs. ImageNet: 2.5x realms, 9x concepts
✅Conciseness: no concept overlapping
✅ReCo: Relational Contrastive Learning
✅New supervised contrastive learning SOTA
More: https://bit.ly/3RJRKU0
🔥Grand Unification of Object Tracking🔥
👉UNICORN: unified method for SOT, MOT, VOS, & MOTS with a single neural net. 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Great unification for 4 tracking tasks
✅Bridging methods / pixel-wise corresp.
✅SOTA on 8 challenging benchmarks
✅Source code under MIT License
More: https://bit.ly/3o74h6g
🔥🔥 Update 🔥🔥
👉Code https://github.com/THUDM/CogVideo
👉Demo https://wudao.aminer.cn/cogvideo/
More: https://bit.ly/3yP86BQ
🍰 Long-Term Object Segmentation 🍰
👉XMem: object segmentation for long clips with unified feature memory stores
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Inspired by Atkinson–Shiffrin model
✅Stores with different temporal scales
✅Memory consolidation algorithm
✅Compact/powerful long-term memory
✅Source code and models available
More: https://bit.ly/3PP0EOn
☀️ 4D Neural Relightable Humans ☀️
👉Relighting4D: free-viewpoints relighting of humans under unknown illuminations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Relight dynamic, free viewpoints
✅Disentangled reflectance/geometry
✅SOTA on synthetic/real datasets
✅Code/models under MIT License
More: https://bit.ly/3RF3yH9
🤹♂️ K-Means Mask Transformer 🤹♂️
👉#Google AI unveils kMaX-DeepLab, novel E2E method for segmentation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅kMaX-DeepLab: k-means Mask Xformer
✅Rethinking relationship pixels / object
✅Cross-attention -> k-means clustering
✅The new SOTA on several dataset
More: https://bit.ly/3O2QV5I
👽 Neural I2I with a few shoots 👽
👉#Alibaba unveils a novel portrait stylization. Limited samples (∼100) -> HD outputs
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Calibration first, translation later
✅Balanced distribution to calibrate bias
✅Spatially semantic constraints via geometry
✅Source code and models soon available!
More: https://bit.ly/3IwOmHO
📟📟AI-Designed Circuits with Deep RL📟📟
👉#Nvidia unveils an #AI to design circuits from scratch, smaller and faster than SOTA ones
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Parallel prefix circuits for Hi-Perf
✅RL framework to explore the circuit space
✅Smaller, Faster, Power-- from the scratch
More: https://bit.ly/3yY9dk7
🦒 Text2LIVE: Text-Driven Neural Editing 🦒
👉#Amazon unveils a novel #AI for text-driven edit of videos. Insane! 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Semantic edits of real-world videos
✅Edit layer–RGBA representing target
✅Edit layers synthesized on single input
✅No masks or a pre-trained generator
More: https://bit.ly/3NVP6aE
😊😎 Seq-DeepFake via Transformers 😎😊
👉S-Lab opens Seq-DeepFake: Detecting Sequential DeepFake Manipulation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Seq-DeepFake: sequences of facial edits
✅Dataset: 85k #deepfake manipulation
✅Powerful Seq-DeepFake Transformer
✅Code, dataset and models available!
More: https://bit.ly/3ACQXhi
