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
Show more📈 Analytical overview of Telegram channel AI with Papers - Artificial Intelligence & Deep Learning
Channel AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) in the English language segment is an active participant. Currently, the community unites 17 261 subscribers, ranking 7 509 in the Technologies & Applications category and 2 130 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 261 subscribers.
According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 261 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 16.91%. Within the first 24 hours after publication, content typically collects 7.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 918 views. Within the first day, a publication typically gains 1 216 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 16.
- Thematic interests: Content is focused on key topics such as framework, object, dataset, tba, depth.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 26 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
17 261
Subscribers
+224 hours
+17 days
+26130 days
Posts Archive
🏛️ 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 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 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 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/
🔥 Code is out 🔥
👉Repo: https://github.com/princeton-computational-imaging/NSF
🔥 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
