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

Malaysia2 240 Technologies & Applications7 726...

📈 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 17 154 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 17 154 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.

17 154

Subscribers

-624 hours

-277 days

-16630 days

4 057

Post views

~ 1 17724 hours

~ 1 35348 hours

23.63%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

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