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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 144 Technologies & Applications7 543...

📈 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 7 543 in the Technologies & Applications category and 2 144 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 26 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 250 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 17.43%. Within the first 24 hours after publication, content typically collects 7.04% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 008 views. Within the first day, a publication typically gains 1 216 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 27 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

+124 hours

+97 days

+25030 days

3 008

Post views

~ 1 21624 hours

~ 1 52548 hours

17.43%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

17 261

🪛 HANDAL: Real-World Manipulable Objects 🪛 👉 #Nvidia unveils HANDAL dataset: category-level object pose and affordance prediction 😎Review https://t.ly/MXZDI 😎Paper arxiv.org/pdf/2308.01477.pdf 😎Dataset https://wenbowen123.github.io/handaldataset/

17 261

🙏 A quick poll for helping me in improving the quality of the contents about #computervision. Please give me a feedback here: https://t.ly/qXb4C Thanks :)

17 261

🎠 Neural Closed-Loop Simulator 🎠 👉A neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle and converts it into a realistic closed-loop multi-sensor simulation 😎Review https://t.ly/EcRLc 😎Paper arxiv.org/pdf/2308.01898.pdf 😎Project https://waabi.ai/unisim/

17 261

📸 Computational Burst Photography in App 📸 👉#Google unveils a novel computational burst system to democratize the professional photography via smartphone 😎Review https://t.ly/5ibJX 😎Paper arxiv.org/pdf/2308.01379.pdf 😎Project https://motion-mode.github.io

17 261

👗 Multimodal Neural Designer 👗 👉 Multimodal #AI that can generate novel fashion images conditioned on text, keypoints, and sketches 😎Review https://t.ly/zVk70 😎Paper arxiv.org/pdf/2304.02051.pdf 😎Code github.com/aimagelab/multimodal-garment-designer

17 261

🥬 Consensus-Adaptive RANSAC 🥬 👉A novel RANSAC that learns to explore the parameter space via a novel attention layer 😎Review https://t.ly/eSLmD 😎Paper arxiv.org/pdf/2307.14030.pdf 😎Code github.com/cavalli1234/CA-RANSAC

17 261

🥬 Consensus-Adaptive RANSAC 🥬 👉A novel RANSAC that learns to explore the parameter space via a novel attention layer

17 261

🐧 Tracking Anything in High Quality 🐧 👉Video multi-object segmenter (VMOS) and a mask refiner (MR) to track anything 😎Review https://t.ly/hAvF2 😎Paper arxiv.org/pdf/2307.13974.pdf 😎Code github.com/jiawen-zhu/HQTrack

17 261

🦀 simPLE: learning to grasp only with CAD 🦀 👉simPLE learns to pick, regrasp & place objects precisely, given only the object CAD model and no prior experience 😎Review https://t.ly/ab5pA 😎Paper arxiv.org/pdf/2307.13133.pdf 😎Project mcube.mit.edu/research/simPLE.html

17 261

🥬 Generative AI’s Next Frontiers 🥬 👉Hair simulation, 2D->3D animation, and much more. ~20 papers from #NVIDIA accepted into #SIGGRAPH2023 😎 Review https://t.ly/wgGin

17 261

🛵ALPR via CTS-Matching 🛵 👉UIT unveils a neural approach (#YOLO5 + tracking + rotation) to improve the license plate recognition accuracy 😎Review https://t.ly/VP4BP 😎Paper arxiv.org/pdf/2307.11336.pdf 😎Code github.com/chequanghuy/Character-Time-series-Matching

17 261

🪛 CAD-based Object Segmentation 🪛 👉 A novel three-stage approach to segment unseen objects in RGB images using their CAD models 😎Review https://t.ly/RtHLN 😎Paper arxiv.org/pdf/2307.11067.pdf 😎Code https://github.com/nv-nguyen/cnos

17 261

🪤 PAPR: Proximity Attention Point Render 🪤 👉PAPR: fast point-based scene representation with differentiable renderer approach 😎Review https://t.ly/yoI0g 😎Paper arxiv.org/pdf/2307.11086.pdf 😎Project https://zvict.github.io/papr

17 261

🪤 PAPR: Proximity Attention Point Render 🪤 👉PAPR: fast point-based scene representation with differentiable renderer approach

17 261

💪 Muscles in Action with #AI 💪 👉Muscles in Action (MIA): learn to incorporate muscle activity into human motion representations 😎Review https://t.ly/hUKub 😎Paper arxiv.org/pdf/2212.02978.pdf 😎Project musclesinaction.cs.columbia.edu

17 261

👩‍🦰 Ultra-Realistic Neural Hair 👩‍🦰 👉A novel method to reconstruct the hair geometry at a strand level from monocular video or multi-view images 😎Review https://t.ly/6xZyp 😎Paper arxiv.org/pdf/2306.05872.pdf 😎Project samsunglabs.github.io/NeuralHaircut 😎Code github.com/SamsungLabs/NeuralHaircut

17 261

🪟 META's Ultra-Realistic Data for #AR🪟 👉Aria Digital Twin: egocentric dataset for object detection/tracking, reconstruction/understanding, S2R learning, human pose prediction and more 😎Review https://t.ly/MRPt1 😎Paper arxiv.org/pdf/2306.06362.pdf 😎Project www.projectaria.com/datasets/adt/ 😎Code github.com/facebookresearch/projectaria_tools

17 261

🍉 AltFreezing: new SOTA in detecting fake-faces 🍉 👉#Microsoft unveils AltFreezing: spatial/temporal artifacts in one model for more general face forgery detection 😎Review https://t.ly/mkIKX 😎Paper https://t.ly/z4KnJ 😎Code github.com/ZhendongWang6/AltFreezing

17 261

🦙 Llama-2: the Open-Source "#chatgpt"🦙 👉GenAI, #Meta unveils Llama-2: a collection of LLMs ranging in scale 7-70B parameters. Challenging with #chatgpt, but open. 😎Review https://t.ly/bLJgP 😎Paper https://t.ly/AOXru 😎Project https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/