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 17 261 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 491-o'rinni va Malayziya mintaqasida 2 128-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 261 obunachiga ega bo‘ldi.
24 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 258 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 16.76% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 7.11% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 893 marta ko‘riladi; birinchi sutkada odatda 1 227 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
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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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 25 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.
17 261
Obunachilar
-224 soatlar
+27 kun
+25830 kun
Postlar arxiv
⚔️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
