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

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马来西亚2 240 技术与应用7 726...

📈 Telegram 频道 AI with Papers - Artificial Intelligence & Deep Learning 的分析概览

频道 AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) 英语语言赛道中的是活跃参与者。目前社区聚集了 17 154 名订阅者，在 技术与应用 类别中位列第 7 726，并在 马来西亚 地区排名第 2 240 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 17 154 名订阅者。

根据 21 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -166，过去 24 小时变化为 -6，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 23.63%。内容发布后 24 小时内通常能获得 6.86% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 4 057 次浏览，首日通常累积 1 177 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 26。
主题关注点： 内容集中在 framework, object, dataset, tba, depth 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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”

凭借高频更新（最新数据采集于 22 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

17 154

订阅者

-624 小时

-277 天

-16630 天

4 057

帖子浏览量

~ 1 17724 小时

~ 1 35348 小时

23.63%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

17 154

🔥 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

17 154

🍾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

17 154

🐸 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

17 154

🤖 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)

17 154

🔥 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

17 154

🪴 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

17 154

🪩 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

17 154

🪅🪅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

17 154

🔥 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

17 154

🌾 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

17 154

🌮 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

17 154

🍦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

17 154

🍦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

17 154

🐻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

17 154

🧬 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

17 154

🤓 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

17 154

🌱 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

17 154

💦 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

17 154

🧤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

17 154

🌵 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