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 17 265 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 17 265 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 29 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 219 و در ۲۴ ساعت گذشته برابر 0 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 16.46% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.09% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 2 842 بازدید دریافت می‌کند. در اولین روز معمولاً 1 223 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 17 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 30 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.
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آرشیو پست ها
🔥PCVOS: clip-wise mask VOS🔥
👉PCVOS: new semi-supervised video object segmentation method
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Reformulating semi-supervised VOS
✅Novel per-clip inference perspective
✅Clip-wise operation on intra-clip
✅PCVOS: model for per-clip inference
✅New SOTA on multiple benchmarks
More: https://bit.ly/3vJtmbz
☀️LocoProp: Neural Layers Composition☀️
👉Google AI unveils LocoProp: novel neural paradigm for modular composition of layers.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Backprop++ via Local Loss Optimization
✅Layer-based w-reg, target output, loss
✅Multiple local update via first-order opt.
✅Superior performance and efficiency
More: https://bit.ly/3Q40YJn
🔥🔥MultiNeRF: three NeRFs are out!🔥🔥
👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Paper_1: Mip-NeRF 360
✅Paper_2: Ref-NeRF
✅Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
🔥 MinVIS, a new SOTA is out 🔥
👉#Nvidia miniVIS: no video-based architectures nor training procedures🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video architecture/train not required
✅MinVIS outperforms the previous SOTA
✅Occluded VIS (OVIS): >10% improvement
✅1% of labeled frames >> fully-supervised
More: https://bit.ly/3pcYzk1
🚀 #VR by NASA - 1985 🚀
👉Q: is #VR the technology that developed least in the last 40 years? 🤔
Let's talk: https://bit.ly/3JxDZ7i
👩🦰 Real-Time Neural Hair 👩🦰
👉Accurate hair geometry & appearance from multi-pics
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Bonn, CMU and Reality Labs
✅Photorealistic Real-Time render
✅HQ strand geometry/appearance
✅Novel scalp texture description
✅Intuitive manipulation of 3D hair
More: https://bit.ly/3vBiH2G
🧣NeRF for Outdoor Scene Relighting🧣
👉NeRF-OSR: the first neural radiance fields approach for outdoor scene relighting
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF-method for outdoor relighting
✅Simultaneous illumination/viewpoint
✅Control over shading, shadow, albedo
✅Self-Supervised training from outdoor
✅Dataset: 3240 viewpoints, 110+ times
More: https://bit.ly/3vBiH2G
🔥🔥UPDATE🔥🔥
Code Released: https://github.com/andreas128/RePaint
🔥 MobileNeRF is out -> Pure Fire! 🔥
👉MobileNeRF is out: the mobile evolution of NeRF via textured polygons.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Same quality, 10x faster than SNeRG
✅Memory-- by storing surface textures
✅Integrated GPUs: less memory/power
✅Suitable for browser & viewer is HTML
More: https://bit.ly/3PUKPWy
🔥AND/OR: Composable Diffusion Models🔥
👉Novel neural compositional generation via Composable Diffusion Models
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DM as energy-based models
✅Connecting diffusion models
✅Conjunction & negation, on top of DM
✅Zero-shot combinatorial generalization
More: https://bit.ly/3PYv1Cs
🚜NeDDF: the NeRF evolution!🚜
👉Novel 3D representation that reciprocally constrains distance & density fields
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF provides no distance
✅Extending for arbitrary density
✅Density via dist-field & gradient
✅Alleviating the instability
More: https://bit.ly/3Bte8LC
🍏🍏 GAUDI: the Neural Architect 🍏🍏
👉Novel generative model for immersive 3D scenes from a moving camera
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hundreds of thousands pics/scenes
✅Novel denoising optimization objective
✅New SOTA across multiple datasets
✅Un/conditional on images/text
More: https://bit.ly/3Bt65ye
🍦🍦 Rewriting Geometry of GAN 🍦🍦
👉Drive GAN synthesizing many unseen objects with the desired shape
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅User-friendly "warping" with geometry
✅Low-rank update to layer for editing
✅Latent augmentation based on style-mix
✅Endless objects with defined changes
✅Latent space interpolation, image editing
More: https://bit.ly/3zIfOj8
🏙️ CityNeRF: Neural Rendering of City Scenes 🏙️
👉Progressive NeRF model and training set on city-scenes
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅BungeeNeRF: novel progressive NeRF
✅Details on drastically varied scales
✅Growing with residual block structure
✅Inclusive multi-level data supervision
More: https://bit.ly/3cS9vk7
🎩ShAPO: SOTA in object understanding🎩
👉Joint multi-object detection, #3D texture, 6D object pose & size estimation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Disentangled shape & appearance
✅Efficient octree-based differentiable
✅Object-centric understanding pipeline
✅Detection, reconstruction , 6D & size
✅SOTA in reconstruction & pose est.
More: https://bit.ly/3oHN5EQ
📺 NeRF-ing "The Big Bang Theory" 📺
👉Berkeley unveils an approach for accurate estimation of actor’s 3D pose & location
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Input: images across the whole season
✅3D context (i.e. cams, structure, body)
✅Integrating context in 3D estimation
✅Re-ID, gaze, cinematography, pic editing
✅Knock, Knock, Penny!
More: https://bit.ly/3OLuaUb
🦊 3D-Aware "StyleGANv2" version 🦊
👉Upgrading StyleGANv2 into a novel 3D-aware GAN with just a minimal set of changes🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MPI-like 3D-aware GAN w/ single-view
✅GMPI: generative multiplane image
✅2D GAN 3D-aware with a minimal changes
✅Encoding 3D-aware inductive biases
More: https://bit.ly/3OJ5gnS
🦚 TinyCD: Neural Change Detection 🦚
👉TinyCD: new SOTA in change detection with up to 150x fewer parameters.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SOTA with up to 150X fewer params
✅Mixing blocks for s.t. cross-correlation
✅PW-MLP for pixel wise classification
✅MAMB: novel block for skip connection
More: https://bit.ly/3zFEngk
⚗️ SemAbs: 3D Scene Understanding ⚗️
👉Framework that equips 2D Vision-Language Models (VLMs) with new 3D spatial capabilities
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅2D VLMs with 3D reasoning skills
✅ViTs Efficient MS Relevancy Extraction
✅Novel Open-World understanding tasks
✅Completing partially observed objects
✅Finding hidden objects from language
More: https://bit.ly/3PYYk7d
🎃New SOTA in UDA Semantic Seg.🎃
👉HRDA: multi-res Unsupervised Domain Adaptive Semantic Seg. -> SOTA
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ETH + MPG + KU Leuven 🤯
✅HRDA: multi-res approach for UDA
✅Manageable GPU memory footprint
✅Small objects & fine segmentation detail
✅New SOTA on GTA and Synthia dataset
More: https://bit.ly/3cKtDEp
