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 7 500 in the Technologies & Applications category and 2 124 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 24 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 258 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 16.76%. Within the first 24 hours after publication, content typically collects 7.11% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 893 views. Within the first day, a publication typically gains 1 227 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 25 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
+27 days
+25830 days
Posts Archive
⚔️SAMurai: SAM for Tracking⚔️
👉UWA unveils SAMURAI, an enhanced adaptation of SAM 2 specifically designed for visual object tracking. New SOTA! Code under Apache 2.0💙
👉Review https://t.ly/yGU0P
👉Paper https://arxiv.org/pdf/2411.11922
👉Repo https://github.com/yangchris11/samurai
👉Project https://yangchris11.github.io/samurai/
🧰 EchoMimicV2: Semi-body Human 🧰
👉Alipay (ANT Group) unveils EchoMimicV2, the novel SOTA half-body human animation via APD-Harmonization. See clip with audio (ZH/ENG). Code & Demo announced💙
👉Review https://t.ly/enLxJ
👉Paper arxiv.org/pdf/2411.10061
👉Project antgroup.github.io/ai/echomimic_v2/
👉Repo-v2 github.com/antgroup/echomimic_v2
👉Repo-v1 https://github.com/antgroup/echomimic
🧶 MagicQuill: super-easy Diffusion Editing 🧶
👉MagicQuill is a novel system designed to support users in smart editing of images. Robust UI/UX (e.g., inserting/erasing objects, colors, etc.) under a multimodal LLM to anticipate user intentions in real time. Code & Demos released 💙
👉Review https://t.ly/hJyLa
👉Paper https://arxiv.org/pdf/2411.09703
👉Project https://magicquill.art/demo/
👉Repo https://github.com/magic-quill/magicquill
👉Demo https://huggingface.co/spaces/AI4Editing/MagicQuill
🛥️ Global Tracklet Association MOT 🛥️
👉A novel universal, model-agnostic method designed to refine and enhance tracklet association for single-camera MOT. Suitable for datasets such as SportsMOT, SoccerNet & similar. Source code released💙
👉Review https://t.ly/gk-yh
👉Paper https://lnkd.in/dvXQVKFw
👉Repo https://lnkd.in/dEJqiyWs
🔥 4 NanoSeconds inference 🔥
👉LogicTreeNet: convolutional differentiable logic gate net. with logic gate tree kernels: Computer Vision into differentiable LGNs. Up to 6100% smaller than SOTA, inference in 4 NANOsecs!
👉Review https://t.ly/GflOW
👉Paper https://lnkd.in/dAZQr3dW
👉Full clip https://lnkd.in/dvDJ3j-u
🐔SeedEdit: foundational T2I🐔
👉ByteDance unveils a novel T2I foundational model capable of delivering stable, high-aesthetic image edits which maintain image quality through unlimited rounds of editing instructions. No code announced but a Demo is online💙
👉Review https://t.ly/hPlnN
👉Paper https://arxiv.org/pdf/2411.06686
👉Project team.doubao.com/en/special/seededit
🤗Demo https://huggingface.co/spaces/ByteDance/SeedEdit-APP
❄️Don’t Look Twice: ViT by RLT❄️
👉CMU unveils RLT: speeding up the video transformers inspired by run-length encoding for data compression. Speed the training up and reducing the token count by up to 80%! Source Code announced 💙
👉Review https://t.ly/ccSwN
👉Paper https://lnkd.in/d6VXur_q
👉Project https://lnkd.in/d4tXwM5T
👉Repo TBA
🫠 X-Portrait 2: SOTA(?) Portrait Animation 🫠
👉ByteDance unveils a preview of X-Portrait2, the new SOTA expression encoder model that implicitly encodes every minuscule expressions from the input by training it on large-scale datasets. Impressive results but no paper & code announced.
👉Review https://t.ly/8Owh9 [UPDATE]
👉Paper ?
👉Project byteaigc.github.io/X-Portrait2/
👉Repo ?
🧠 Single Neuron Reconstruction 🧠
👉SIAT unveils NeuroFly, a framework for large-scale single neuron reconstruction. Formulating neuron reconstruction task as a 3-stage streamlined workflow: automatic segmentation - connection - manual proofreading. Bridging computer vision and neuroscience 💙
👉Review https://t.ly/Y5Xu0
👉Paper https://arxiv.org/pdf/2411.04715
👉Repo github.com/beanli161514/neurofly
💪 Muscles in Time Dataset 💪
👉Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. MinT contains 9+ hours of simulation data covering 227 subjects and 402 simulated muscle strands. Code & Dataset available soon 💙
👉Review https://t.ly/108g6
👉Paper arxiv.org/pdf/2411.00128
👉Project davidschneider.ai/mint
👉Code github.com/simplexsigil/MusclesInTime
🏣 CityGaussianV2: Large-Scale City 🏣
👉A novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency: 10x compression, 25% faster & -50% memory! Source code released💙
👉Review https://t.ly/Xgn59
👉Paper arxiv.org/pdf/2411.00771
👉Project dekuliutesla.github.io/CityGaussianV2/
👉Code github.com/DekuLiuTesla/CityGaussian
☀️ Universal Relightable Avatars ☀️
👉#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results!
👉Review https://t.ly/U-ESX
👉Paper arxiv.org/pdf/2410.24223
👉Project junxuan-li.github.io/urgca-website
☀️ Universal Relightable Avatars ☀️
👉#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results!
👉Review https://t.ly/U-ESX
👉Paper arxiv.org/pdf/2410.24223
👉Project junxuan-li.github.io/urgca-website
🍜 REM: Segment What You Describe 🍜
👉REM is a framework for segmenting concepts in video that can be described via LLM. Suitable for rare & non-object dynamic concepts, such as waves, smoke, etc. Code & Data announced 💙
👉Review https://t.ly/OyVtV
👉Paper arxiv.org/pdf/2410.23287
👉Project https://miccooper9.github.io/projects/ReferEverything/
🔥🔥 The code is out 🔥🔥
👉Code https://github.com/HaixinShi/fmov_pose
🔥 D-FINE: new SOTA Detector 🔥
👉D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR model. New SOTA on MS COCO with additional data. Code & models available 💙
👉Review https://t.ly/aw9fN
👉Paper https://arxiv.org/pdf/2410.13842
👉Code https://github.com/Peterande/D-FINE
🫐 Blendify: #Python + Blender 🫐
👉Lightweight Python framework that provides a high-level API for creating & rendering scenes with #Blender. It simplifies data augmentation & synthesis. Source Code released💙
👉Review https://t.ly/l0crA
👉Paper https://arxiv.org/pdf/2410.17858
👉Code https://virtualhumans.mpi-inf.mpg.de/blendify/
⛈️ SMITE: SEGMENT IN TIME ⛈️
👉SFU unveils SMITE: a novel AI that -with only one or few segmentation references with fine granularity- is able to segment different unseen videos respecting the segmentation references. Dataset & Code (under Apache 2.0) announced 💙
👉Review https://t.ly/w6aWJ
👉Paper arxiv.org/pdf/2410.18538
👉Project segment-me-in-time.github.io/
👉Repo github.com/alimohammadiamirhossein/smite
🌻 Plant Camouflage Detection🌻
👉PlantCamo Dataset is the first dataset for plant camouflage detection: 1,250 images with camouflage characteristics. Source Code released 💙
👉Review https://t.ly/pYFX4
👉Paper arxiv.org/pdf/2410.17598
👉Code github.com/yjybuaa/PlantCamo
🪁 PL2Map: efficient neural 2D-3D 🪁
👉PL2Map is a novel neural network tailored for efficient representation of complex point & line maps. A natural representation of 2D-3D correspondences
👉Review https://t.ly/D-bVD
👉Paper arxiv.org/pdf/2402.18011
👉Project https://thpjp.github.io/pl2map
👉Code https://github.com/ais-lab/pl2map
