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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
Ko'proq ko'rsatish📈 Telegram kanali AI with Papers - Artificial Intelligence & Deep Learning analitikasi
AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 17 137 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 702-o'rinni va Malayziya mintaqasida 2 235-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 137 obunachiga ega bo‘ldi.
24 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -197 ga, so‘nggi 24 soatda esa -7 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 25.73% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.87% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 4 411 marta ko‘riladi; birinchi sutkada odatda 1 177 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 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 25 Iyun, 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.
17 137
Obunachilar
-724 soatlar
-427 kunlar
-19730 kunlar
Postlar arxiv
🦀 simPLE: learning to grasp only with CAD 🦀
👉simPLE learns to pick, regrasp & place objects precisely, given only the object CAD model and no prior experience
😎Review https://t.ly/ab5pA
😎Paper arxiv.org/pdf/2307.13133.pdf
😎Project mcube.mit.edu/research/simPLE.html
🥬 Generative AI’s Next Frontiers 🥬
👉Hair simulation, 2D->3D animation, and much more. ~20 papers from #NVIDIA accepted into #SIGGRAPH2023
😎 Review https://t.ly/wgGin
🛵ALPR via CTS-Matching 🛵
👉UIT unveils a neural approach (#YOLO5 + tracking + rotation) to improve the license plate recognition accuracy
😎Review https://t.ly/VP4BP
😎Paper arxiv.org/pdf/2307.11336.pdf
😎Code github.com/chequanghuy/Character-Time-series-Matching
🪛 CAD-based Object Segmentation 🪛
👉 A novel three-stage approach to segment unseen objects in RGB images using their CAD models
😎Review https://t.ly/RtHLN
😎Paper arxiv.org/pdf/2307.11067.pdf
😎Code https://github.com/nv-nguyen/cnos
🪤 PAPR: Proximity Attention Point Render 🪤
👉PAPR: fast point-based scene representation with differentiable renderer approach
😎Review https://t.ly/yoI0g
😎Paper arxiv.org/pdf/2307.11086.pdf
😎Project https://zvict.github.io/papr
🪤 PAPR: Proximity Attention Point Render 🪤
👉PAPR: fast point-based scene representation with differentiable renderer approach
💪 Muscles in Action with #AI 💪
👉Muscles in Action (MIA): learn to incorporate muscle activity into human motion representations
😎Review https://t.ly/hUKub
😎Paper arxiv.org/pdf/2212.02978.pdf
😎Project musclesinaction.cs.columbia.edu
👩🦰 Ultra-Realistic Neural Hair 👩🦰
👉A novel method to reconstruct the hair geometry at a strand level from monocular video or multi-view images
😎Review https://t.ly/6xZyp
😎Paper arxiv.org/pdf/2306.05872.pdf
😎Project samsunglabs.github.io/NeuralHaircut
😎Code github.com/SamsungLabs/NeuralHaircut
🪟 META's Ultra-Realistic Data for #AR🪟
👉Aria Digital Twin: egocentric dataset for object detection/tracking, reconstruction/understanding, S2R learning, human pose prediction and more
😎Review https://t.ly/MRPt1
😎Paper arxiv.org/pdf/2306.06362.pdf
😎Project www.projectaria.com/datasets/adt/
😎Code github.com/facebookresearch/projectaria_tools
🍉 AltFreezing: new SOTA in detecting fake-faces 🍉
👉#Microsoft unveils AltFreezing: spatial/temporal artifacts in one model for more general face forgery detection
😎Review https://t.ly/mkIKX
😎Paper https://t.ly/z4KnJ
😎Code github.com/ZhendongWang6/AltFreezing
🦙 Llama-2: the Open-Source "#chatgpt"🦙
👉GenAI, #Meta unveils Llama-2: a collection of LLMs ranging in scale 7-70B parameters. Challenging with #chatgpt, but open.
😎Review https://t.ly/bLJgP
😎Paper https://t.ly/AOXru
😎Project https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/
☔ #SelfDriving? It's all about weather! ☔
👉Novel self-supervised MDE method to handle adverse weather in real-world autonomous driving
😎Review https://t.ly/tcLQW
😎Paper arxiv.org/pdf/2307.08357.pdf
😎Project kieran514.github.io/Robust-Depth-Project/
🐈 Gen-AI as representation learner 🐈
👉DreamTeacher: novel self-supervised feats. representation learning framework that utilizes gen-nets for pre-training downstream image backbones
😎Review https://t.ly/RL8iG
😎Paper arxiv.org/pdf/2307.07487.pdf
😎Project research.nvidia.com/labs/toronto-ai/DreamTeacher
🧯 Neural Focal Modulation for VAR 🧯
👉Video-FocalNet is a novel architecture for video recognition that models both local and global context
😎Review https://t.ly/rF_fk
😎Paper arxiv.org/pdf/2307.06947.pdf
😎Project talalwasim.github.io/Video-FocalNets
😎Code github.com/TalalWasim/Video-FocalNets
💡DATID-3D: Diffusive Text-to-3D Generation💡
👉 A novel domain adaptation method for 3D via text-to-image diffusion. 🤗-Demo available!
😎Review https://t.ly/ecBvM
😎Paper arxiv.org/pdf/2211.16374.pdf
😎Project gwang-kim.github.io/datid_3d/
😎Code github.com/gwang-kim/DATID-3D
🤗Demo huggingface.co/spaces/gwang-kim/DATID-3D
😎Colab colab.research.google.com/drive/1e9NSVB7x_hjz-nr4K0jO4rfTXILnNGtA?usp=sharing
🎪 Extreme Human Pose Estimation 🎪
👉RePoGen: novel synthetic data generator of extreme/realistic poses of humans
😎Review https://t.ly/ecBvM
😎Paper arxiv.org/pdf/2307.06737.pdf
😎Project mirapurkrabek.github.io/RePoGen-paper
😎Code github.com/MiraPurkrabek/RePoGen
🃏 Deepfake via casual self-scan 🃏
👉TAU presents a novel approach to reenact an ID using only a casual self-scan
😎Review https://t.ly/9T8Wi
😎Paper arxiv.org/pdf/2307.06307.pdf
😎Project arielazary.github.io/PGR
🔥o-TTT: Test-Time Training on fire 🔥
👉Extending the TTT to the streaming setting. Suitable for Panoptic, Instance & Colorization.
😎Review https://t.ly/eZYA
😎Paper arxiv.org/pdf/2307.05014.pdf
😎Project https://video-ttt.github.io/
😎Code github.com/renwang435/video-ttt-release
🍡 Text2Cinemagraphs: Cinemagraph from text 🍡
👉CMU (+ #Snap) unveils a fully automated method for creating cinemagraphs from text descriptions
😎Review https://t.ly/BwZs6
😎Paper arxiv.org/pdf/2307.03190.pdf
😎Project text2cinemagraph.github.io/website/
😎Code github.com/text2cinemagraph/text2cinemagraph
🛣️ STAR.: 3D-tracking w/ attention paradigm 🛣️
👉#Mercedes STAR: e2e 3D object tracking that follows the tracking-by-attention paradigm
😎Review https://t.ly/JoGj
😎Paper arxiv.org/pdf/2306.17602.pdf
😎Project simondoll.github.io/publications/star_track
