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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
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 17 151 subscribers, ranking 7 726 in the Technologies & Applications category and 2 240 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 151 subscribers.
According to the latest data from 21 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -166 over the last 30 days and by -6 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 23.63%. Within the first 24 hours after publication, content typically collects 6.86% reactions from the total number of subscribers.
- Post reach: On average, each post receives 4 057 views. Within the first day, a publication typically gains 1 177 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 26.
- 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 22 June, 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.
17 151
Subscribers
-624 hours
-277 days
-16630 days
Posts Archive
💦 ObjectDrop: automagical objects removal 💦
👉#Google unveils ObjectDrop, the new SOTA in photorealistic object removal and insertion. Focus on shadows and reflections, impressive!
👉Review https://t.ly/ZJ6NN
👉Paper https://arxiv.org/pdf/2403.18818.pdf
👉Project https://objectdrop.github.io/
🏀 MAVOS Object Segmentation 🏀
👉MAVOS is a transformer-based VOS that introduces a novel, optimized and dynamic long-term modulated cross-attention memory. Code & Models announced (coming soon under BSD 3-Clause)💙
👉Review https://t.ly/SKaRG
👉Paper https://lnkd.in/dQyifKa3
👉Project github.com/Amshaker/MAVOS
👉Code/Demo (announced)
☔ AiOS: All-in-One-Stage Humans ☔
👉All-in-one-stage framework for SOTA multiple expressive pose and shape recovery without additional human detection step.
👉Review https://t.ly/ekNd4
👉Paper https://arxiv.org/pdf/2403.17934.pdf
👉Project https://ttxskk.github.io/AiOS/
👉Code/Demo (announced)
💄TinyBeauty: 460 FPS Diffusion Make-up💄
👉TinyBeauty: only 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile!
👉Review https://t.ly/LG5ok
👉Paper https://arxiv.org/pdf/2403.15033.pdf
👉Project https://tinybeauty.github.io/TinyBeauty/
💄💄TinyBeauty: 460 FPS Diffusion Make-up💄💄
👉TinyBeauty;:necessitates merely 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile! Authors: Jiao Tong University, Alibaba, USC-SJTU.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DAL, Data Amplify Learning: novel learning framework
✅Diffusion-based Data Amplifier for better training
✅Only 80K parameters to achieve the previous SOTA
✅Insane inference speed (460 fps) on iPhone 13
✅Highly competitive using only FIVE image pairs
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2403.15033.pdf
👉Project https://tinybeauty.github.io/TinyBeauty/
🦖 T-Rex 2: a new SOTA is out! 🦖
👉A novel (VERY STRONG) open-set object detector model. Strong zero-shot capabilities, suitable for various scenarios with only one suit of weights. Demo and Source Code released💙
👉Review https://t.ly/fYw8D
👉Paper https://lnkd.in/dpmRh2zh
👉Project https://lnkd.in/dnR_jPcR
👉Code https://lnkd.in/dnZnGRUn
👉Demo https://lnkd.in/drDUEDYh
🦕 DINO-based Video Tracking 🦕
👉The Weizmann Institute announced the new SOTA in point-tracking via pre-trained DINO features. Source code announced (not yet released)💙
👉Review https://t.ly/_GIMT
👉Paper https://lnkd.in/dsGVDcar
👉Project dino-tracker.github.io/
👉Code (announced)
🪼FaceXFormer: Unified Face-Transformer🪼
👉FaceXFormer, the first unified transformer for facial analysis: face parsing, landmark detection, head pose, attributes recognition, age, gender, race, and landmarks.
👉Review https://t.ly/MfAFI
👉Paper https://arxiv.org/pdf/2403.12960.pdf
👉Project kartik-3004.github.io/facexformer_web/
👉Code github.com/Kartik-3004/facexformer
🏷️ Face Foundation Model 🏷️
👉Arc2Face, the first foundation model for human faces. Large dataset of high-resolution faces with consistent ID / intra-class variability, and an ID-conditioned face model trained on it. Source Code released 💙
👉Review https://t.ly/MfAFI
👉Paper https://lnkd.in/dViE_tCd
👉Project https://lnkd.in/d4MHdEZK
👉Code https://lnkd.in/dv9ZtDfA
🏷️🏷️Arc2Face: Face Foundation Model🏷️🏷️
👉Arc2Face, the first foundation model for human faces. Large dataset of high-resolution faces with consistent ID / intra-class variability, and an ID-conditioned face model trained on it. Source Code released 💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://lnkd.in/dViE_tCd
👉Project https://lnkd.in/d4MHdEZK
👉Code https://lnkd.in/dv9ZtDfA
🪖RT Humanoid from Head-Mounted Sensors🪖
👉#META (+CMU) announced SimXR, a method for controlling a simulated avatar from info obtained from AR/VR headsets
👉Review https://t.ly/Si2Mp
👉Paper arxiv.org/pdf/2403.06862.pdf
👉Project www.zhengyiluo.com/SimXR/
👺 Can GPT-4 play DOOM? 👺
👉Apparently yes, GPT-4 can play the game to a passable degree: it is able to manipulate doors, combat enemies, and perform pathing. Code (with licensing restrictions) released
👉Review https://t.ly/W8-0F
👉Paper https://lnkd.in/dmsB7bjA
👉Project https://lnkd.in/ddDPwjQB
🏛️ 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
