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AI with Papers - Artificial Intelligence & Deep Learning

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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📈 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 17 259 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 509-o'rinni va Malayziya mintaqasida 2 130-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 17 259 obunachiga ega bo‘ldi.

25 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 261 ga, so‘nggi 24 soatda esa 2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 16.91% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 7.04% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 2 918 marta ko‘riladi; birinchi sutkada odatda 1 216 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 16 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 26 Oktabr, 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.

17 259
Obunachilar
+224 soatlar
+17 kun
+26130 kun
Postlar arxiv
🏛️ PIXART-Σ: 4K Generation 🏛️ 👉PixArt-Σ is a novel Diffusion Transformer model (DiT) capable of directly generating images at 4K resolution. Authors: #Huawei, Dalian, HKU & HKUST. Demos available, code announced 💙 👉Review https://t.ly/Cm2Qh 👉Paper arxiv.org/pdf/2403.04692.pdf 👉Project pixart-alpha.github.io/PixArt-sigma-project/ 👉Repo (empty) github.com/PixArt-alpha/PixArt-sigma 🤗-Demo https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha

🦁StableDrag: Point-based Editing🦁 👉#Tencent unveils StableDrag, a novel point-based image editing framework via discrimina
🦁StableDrag: Point-based Editing🦁 👉#Tencent unveils StableDrag, a novel point-based image editing framework via discriminative point tracking method + confidence-based latent enhancement strategy for motion supervision. Source Code announced but still no repo. 👉Review https://t.ly/eUI05 👉Paper https://lnkd.in/dz8-ymck 👉Project stabledrag.github.io/

🧵E-LoFTR: new Feats-Matching SOTA🧵 👉A novel LoFTR-inspired algorithm for efficiently producing semidense matches across images: up to 2.5× faster than LoFTR, superior to previous SOTA pipeline (SuperPoint + LightGlue). Code announced. 👉Review https://t.ly/7SPmC 👉Paper https://arxiv.org/pdf/2403.04765.pdf 👉Project https://zju3dv.github.io/efficientloftr/ 👉Repo https://github.com/zju3dv/efficientloftr

🔥 SOTA: Stable Diffusion 3 is out! 🔥 👉Stable Diffusion 3 is the new SOTA in text-to-image generation (based on human prefe
🔥 SOTA: Stable Diffusion 3 is out! 🔥 👉Stable Diffusion 3 is the new SOTA in text-to-image generation (based on human preference evaluations). New Multimodal Diffusion Transformer (MMDiT) architecture uses separate sets of weights for image & language, improving text understanding/spelling capabilities. Weights & Source Code released 💙 👉Review https://t.ly/a1koo 👉Paper https://lnkd.in/d4i-9Bte 👉Blog https://lnkd.in/d-bEX-ww

💥 MM-AU: Accident Understanding 💥 👉MM-AU - Multi-Modal Accident Video Understanding: 11,727 videos with temporally aligned text descriptions. 2.23M+ BBs and 58,650 pairs of video-based accident reasons. Dataset & Code released 💙 👉Review https://t.ly/a-jKI 👉Paper https://arxiv.org/pdf/2403.00436.pdf 👉Dataset http://www.lotvsmmau.net/MMAU/demo

💌 Multi-LoRA Composition 💌 👉Two novel training-free image composition: LoRA Switch and LoRA Composite for integrating any number of elements in an image through multi-LoRA composition. Source Code released 💙 👉Review https://t.ly/GFy3Z 👉Paper arxiv.org/pdf/2402.16843.pdf 👉Code github.com/maszhongming/Multi-LoRA-Composition

🎷EMO: talking/singing Gen-AI 🎷 👉#Alibaba announced EMO: audio-driven portrait-video generation. Vocal avatar videos with expressive facial expressions, and various head poses. Input: 1 single frame, video duration according to the length of input audio 👉Review https://t.ly/4IYj5 👉Paper https://lnkd.in/dGPX2-Yc 👉Project https://lnkd.in/dyf6p_N3 👉Repo (empty) github.com/HumanAIGC/EMO

🎷EMO: talking/singing Gen-AI 🎷 👉#Alibaba announced EMO: audio-driven portrait-video generation. Vocal avatar videos with expressive facial expressions, and various head poses. Input: 1 single frame, video duration according to the length of input audio 👉Review 👉Paper https://lnkd.in/dGPX2-Yc 👉Project https://lnkd.in/dyf6p_N3 👉Repo (empty) github.com/HumanAIGC/EMO

🫅FlowMDM: Human Composition🫅 👉FlowMDM, a diffusion-based approach capable of generating seamlessly continuous sequences of human motion from textual descriptions. 👉Review https://t.ly/pr2g_ 👉Paper https://lnkd.in/daYRftdF 👉Project https://lnkd.in/dcRkv5Pc 👉Repo https://lnkd.in/dw-3JJks

🗃️ MATH-Vision Dataset 🗃️ 👉MATH-V is a curated dataset of 3,040 HQ mat problems with visual contexts sourced from real mat
🗃️ MATH-Vision Dataset 🗃️ 👉MATH-V is a curated dataset of 3,040 HQ mat problems with visual contexts sourced from real math competitions. Dataset released 💙 👉Review https://t.ly/gmIAu 👉Paper arxiv.org/pdf/2402.14804.pdf 👉Project mathvision-cuhk.github.io/ 👉Code github.com/mathvision-cuhk/MathVision

🩻 Pose via Ray Diffusion 🩻 👉Novel distributed representation of camera pose that treats a camera as a bundle of rays. Naturally suited for set-level transformers, it's the new SOTA on camera pose estimation. Source code released 💙 👉Review https://t.ly/qBsFK 👉Paper arxiv.org/pdf/2402.14817.pdf 👉Project jasonyzhang.com/RayDiffusion 👉Code github.com/jasonyzhang/RayDiffusion

🦥Neuromorphic Video Binarization🦥 👉 University of HK unveils the new SOTA in event-based neuromorphic binary reconstruction: stunning results on QR Code, barcode, & Text. Real-Time, only CPU, up to 10,000 FPS! 👉Review https://t.ly/V-NFa 👉Paper arxiv.org/pdf/2402.12644.pdf 👉Project github.com/eleboss/EBR

🪟 BOG: Fine Geometric Viewshttps://t.ly/E6T0W 🪟 👉 #Google (+Tübingen) unveils Binary Opacity Grids, a novel method to reconstruct triangle meshes from multi-view images able to capture fine geometric detail such as leaves, branches & grass. New SOTA, real-time on Google Pixel 8 Pro (and similar). 👉Review https://t.ly/E6T0W 👉Paper https://lnkd.in/dQEq3zy6 👉Project https://lnkd.in/dYYCadx9 👉Demo https://lnkd.in/d92R6QME

☀️ One2Avatar: Pic -> 3D Avatar ☀️ 👉#Google presents a new approach to generate animatable photo-realistic avatars from only a few/one image. Impressive results. 👉Review https://t.ly/AS1oc 👉Paper arxiv.org/pdf/2402.11909.pdf 👉Project zhixuany.github.io/one2avatar_webpage/

🔥 Breaking: GEMINI 1.5 is out 🔥 👉Gemini 1.5 just announced: standard 128,000 token context window, up to 1 MILLION tokens via AI-Studio and #Vertex AI in private preview 🫠 👉Review https://t.ly/Vblvx 👉More: https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/#build-experiment

🆔 Magic-Me: ID-Specific Video 🆔 👉#ByteDance VCD: with just a few images of a specific identity it can generate temporal consistent videos aligned with the given prompt 👉Review https://t.ly/qjJ2O 👉Paper arxiv.org/pdf/2402.09368.pdf 👉Project magic-me-webpage.github.io 👉Code github.com/Zhen-Dong/Magic-Me

🍇 Graph Neural Network in TF 🍇 👉#Google released TensorFlow-GNN: a novel library to build Graph Neural Networks on the TensorFlow platform. Source Code released under Apache 2.0 license 💙 #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Review https://t.ly/TQfg- 👉Code https://github.com/tensorflow/gnn 👉Blog https://blog.research.google/2024/02/graph-neural-networks-in-tensorflow.html

🌴 Direct-a-Video Generation 🌴 👉Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion 👉Review https://t.ly/dZSLs 👉Paper arxiv.org/pdf/2402.03162.pdf 👉Project https://direct-a-video.github.io/

🌆EfficientViT-SAM: 69x Faster SAM 🌆 👉EfficientViT-SAM is a new family of accelerated Segment Anything Models. The same old SAM’s lightweight prompt encoder and mask decoder, while replacing the heavy image encoder with EfficientViT. Up to 69x faster, source code released 💙 Authors: Tsinghua, MIT & #Nvidia💥 👉Review https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2402.05008.pdf 👉Code github.com/mit-han-lab/efficientvit