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
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AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 17 262 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 510-o'rinni va Malayziya mintaqasida 2 073-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 262 obunachiga ega bo‘ldi.
08 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 20 ga, so‘nggi 24 soatda esa -6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 17.76% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.88% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 064 marta ko‘riladi; birinchi sutkada odatda 1 187 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 12 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent framework, object, dataset, tba, depth kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 09 Noyabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
17 262
Obunachilar
-624 soatlar
-127 kun
+2030 kun
Postlar arxiv
🎃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
🔥🔥 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
