Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
AI with Papers - Artificial Intelligence & Deep Learning
Kanalga Telegram’da o‘tish
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 17 154 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 726-o'rinni va Malayziya mintaqasida 2 240-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 154 obunachiga ega bo‘ldi.
21 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -166 ga, so‘nggi 24 soatda esa -6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 23.63% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.86% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 4 057 marta ko‘riladi; birinchi sutkada odatda 1 177 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 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 22 Iyun, 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.
17 154
Obunachilar
-624 soatlar
-277 kunlar
-16630 kunlar
Postlar arxiv
🔥 940+ FPS Multi-Person Pose Estimation 🔥
👉RTMW (Real-Time Multi-person Whole-body pose estimation models) is a series of high-performance models for 2D/3D whole-body pose estimation. Impressive 940+ FPS on #GPU. Code & models available💙
👉Review https://t.ly/XkBmg
👉Paper arxiv.org/pdf/2407.08634
👉Repo github.com/open-mmlab/mmpose/tree/main/projects/rtmpose
🍾TAPVid-3D: benchmark for TAP-3D🍾
👉#Deepmind (+College London & Oxford) introduces TAPVid-3D, a new benchmark for evaluating long-range Tracking Any Point in 3D: 4,000+ real-world videos, composed of three different data sources spanning a variety of object types, motion patterns, and indoor/outdoor environments. Data & Code available, Apache 2.0💙
👉Review https://t.ly/SsptD
👉Paper https://arxiv.org/pdf/2407.05921
👉Project https://tapvid3d.github.io/
👉Code github.com/google-deepmind/tapnet/tree/main/tapnet/tapvid3d
🐸 Tracking Everything via Decomposition 🐸
👉Hefei unveils a novel decoupled representation that divides static scenes and dynamic objects in terms of motion and appearance. A more robust tracking through occlusions and deformations. Source Code announced under MIT License💙
👉Review https://t.ly/OsFTO
👉Paper https://arxiv.org/pdf/2407.06531
👉Repo github.com/qianduoduolr/DecoMotion
🤖 CODERS: Stereo Detection, 6D & Shape 🤖
👉CODERS: one-stage approach for Category-level Object Detection, pose Estimation and Reconstruction from Stereo images. Source Code announced💙
👉Review https://t.ly/Xpizz
👉Paper https://lnkd.in/dr5ZxC46
👉Repo (TBA)
🔥 Segment Any 4D Gaussians 🔥
👉SA4G is a novel framework to segment anything in #4D Gaussians world. HQ segmentation within seconds in 4D Gaussians and remove, recolor, compose, and render HQ anything masks. Source Code available within August 2024💙
👉Review https://t.ly/uw3FS
👉Paper https://arxiv.org/pdf/2407.04504
👉Project https://jsxzs.github.io/sa4d/
👉Repo https://github.com/hustvl/SA4D
🪴 CAVIS: SOTA Context-Aware Segmentation🪴
👉DGIST unveils the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. It's the new SOTA in several benchmarks. Source Code announced💙
👉Review https://t.ly/G5obN
👉Paper arxiv.org/pdf/2407.03010
👉Repo github.com/Seung-Hun-Lee/CAVIS
👉Project seung-hun-lee.github.io/projects/CAVIS
🪩 MimicMotion: HQ Motion Generation 🪩
👉#Tencent opens a novel controllable video generation framework, dubbed MimicMotion, which can generate HQ videos of arbitrary length mimicking specific motion guidance. Source Code available💙
👉Review https://t.ly/XFoin
👉Paper arxiv.org/pdf/2406.19680
👉Project https://lnkd.in/eW-CMg_C
👉Code https://lnkd.in/eZ6SC2bc
🪅🪅Anomaly Object-Detection🪅🪅
👉The University of Edinburgh introduces a novel anomaly detection problem that focuses on identifying ‘odd-looking’ objects relative to the other instances within a multiple-views scene. Code announced💙
👉Review https://t.ly/3dGHp
👉Paper arxiv.org/pdf/2406.20099
👉Repo https://lnkd.in/d9x6FpUq
🔥 Depth Anything v2 is out! 🔥
👉 Depth Anything V2: outperforming V1 in robustness and fine-grained details. Trained from 595K synthetic labeled images and 62M+ real unlabeled images, the new SOTA in monocular depth estimation (MDE). Code & Models available💙
👉Review https://t.ly/QX9Nu
👉Paper arxiv.org/pdf/2406.09414
👉Project depth-anything-v2.github.io/
👉Repo github.com/DepthAnything/Depth-Anything-V2
👉Data huggingface.co/datasets/depth-anything/DA-2K
🌾 LLaNA: a NeRF-LLM assistant 🌾
👉UniBO unveils LLaNA; novel Multimodal-LLM that understands and reasons on an input NeRF. It processes directly the NeRF weights and performs tasks such as captioning, Q&A, & zero-shot classification of NeRFs.
👉Review https://t.ly/JAfhV
👉Paper arxiv.org/pdf/2406.11840
👉Project andreamaduzzi.github.io/llana/
👉Code & Data coming
🌮 MeshAnything with Transformers 🌮
👉MeshAnything converts any 3D representation into Artist-Created Meshes (AMs), i.e., meshes created by human artists. It can be combined with various 3D asset production pipelines, such as 3D reconstruction and generation, to transform their results into AMs that can be seamlessly applied in the 3D industry. Source Code available💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Review https://t.ly/HvkD4
👉Paper arxiv.org/pdf/2406.10163
👉Code github.com/buaacyw/MeshAnythinghttps://t.ly/HvkD4
🍦Geometry Guided Depth Estimation🍦
👉A novel system for depth estimation and #3D reconstruction which can take as input, where available, previously-made estimates of the scene’s geometry
👉Review https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2406.18387
👉Repo (empty) https://github.com/nianticlabs/DoubleTake
🍦Geometry Guided Depth Estimation🍦
👉#Niantic (+ULC) unveils a novel system for depth estimation and #3D reconstruction which can take as input, where available, previously-made estimates of the scene’s geometry. Source Code announced💙
👉Review https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2406.18387
👉Repo (empty) https://github.com/nianticlabs/DoubleTake
🐻StableNormal: Stable/Sharp Normal🐻
👉Alibaba unveils StableNormal, a novel method which tailors the diffusion priors for monocular normal estimation. Hugging Face demo is available💙
👉Review https://t.ly/FPJlG
👉Paper https://arxiv.org/pdf/2406.16864
👉Demo https://huggingface.co/Stable-X
🧬 Event-driven SuperResolution 🧬
👉USTC unveils EvTexture, the first VSR method that utilizes event signals for texture enhancement. It leverages high-frequency details of events to better recover texture regions in VSR. Source Code available💙
👉Review https://t.ly/zlb4c
👉Paper arxiv.org/pdf/2406.13457
👉Code github.com/DachunKai/EvTexture
🤓 Glasses-Removal from Videos🤓
👉Lightricks unveils a novel method able to receive an input video of a person wearing glasses, and consistently removes the glasses, while preserving the ID. It works even when there are reflections, heavy makeup, and eye blinks. Code announced, not yet released💙
👉Review https://t.ly/Hgs2d
👉Paper arxiv.org/pdf/2406.14510
👉Project https://v-lasik.github.io/
👉Code github.com/v-lasik/v-lasik-code
🌱 TokenHMR : new 3D human pose SOTA 🌱
👉TokenHMR is the new SOTA HPS method mixing 2D keypoints and 3D pose accuracy, thus leveraging Internet data without known camera parameters. It's the new SOTA by a large margin.
👉Review https://t.ly/K9_8n
👉Paper arxiv.org/pdf/2404.16752
👉Project tokenhmr.is.tue.mpg.de/
👉Code github.com/saidwivedi/TokenHMR
💦 Self-driving in wet conditions 💦
👉BMW SemanticSpray: novel dataset contains scenes in wet surface conditions captured by camera, LiDAR and radar. Camera: 2D Boxes | LiDAR: 3D Boxes, Semantic Labels | Radar: Semantic Labels.
👉Review https://t.ly/8S93j
👉Paper https://lnkd.in/dnN5MCZC
👉Project https://lnkd.in/dkUaxyEF
👉Data https://lnkd.in/ddhkyXv8
🧤HOT3D Hand/Object Tracking🧤
👉#Meta opens a novel egocentric dataset for 3D hand & object tracking. A new benchmark for vision-based understanding of 3D hand-object interactions. Dataset available 💙
👉Review https://t.ly/cD76F
👉Paper https://lnkd.in/e6_7UNny
👉Data https://lnkd.in/e6P-sQFK
🌵 RobustSAM for Degraded Images 🌵
👉RobustSAM, the evolution of SAM for degraded images; enhancing the SAM’s performance on low-quality images while preserving prompt-ability & zeroshot generalization. Dataset & Source Code released💙
👉Review https://t.ly/mnyyG
👉Paper arxiv.org/pdf/2406.09627
👉Project robustsam.github.io
👉Code github.com/robustsam/RobustSAM
