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
Show more📈 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 17 262 subscribers, ranking 7 491 in the Technologies & Applications category and 2 128 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 262 subscribers.
According to the latest data from 23 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 255 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 18.93%. Within the first 24 hours after publication, content typically collects 7.17% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 267 views. Within the first day, a publication typically gains 1 237 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 20.
- 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 24 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.
17 262
Subscribers
+224 hours
+67 days
+25530 days
Posts Archive
👽DAM for SAM2 Tracking👽
👉From the University of Ljubljana a novel distractor-aware drop-in memory module for SAM2. Reducing the tracking drift toward distractors and improves redetection capability after object occlusions. DAM4SAM outperforms SAM2.1, SOTA on 10 benchmarks. Repo released 💙
👉Review https://t.ly/8aR59
👉Paper https://arxiv.org/pdf/2509.13864
👉Project jovanavidenovic.github.io/dam-4-sam/
👉Repo github.com/jovanavidenovic/DAM4SAM
I’m keeping the channel free from users’ interactions to avoid SPAM. The only way to interact is commenting in the subchannels after being accepted. Do you like this setting?
I’m keeping the channel free from interaction to avoid SPAM. The only way to interact is commenting the post after being accepted in the subchannel. Do you like this setting?
🍕 Superpixel Anything (SOTA) 🍕
👉 SuperPixel Anything Model, a versatile framework for segmenting images. Extracting image features for superpixel generation blended with a large-scale pretrained model for semantic-agnostic segmentation to ensure superpixels alignement with masks. Damn romantic. Repo & Dataset available💙
👉Review https://t.ly/rpxRh
👉Paper arxiv.org/pdf/2509.12791
👉Repo github.com/waldo-j/spam
🛡️3D Prompted Vision-LLM🛡️
👉#Nvidia unveils SR-3D a novel aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. Flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. Code & Dataset announced💙
👉Review https://t.ly/5Y2c5
👉Paper https://arxiv.org/pdf/2509.13317
👉Project https://www.anjiecheng.me/sr3d
👉Repo TBA
🔥🔥 How We Use ChatGPT 🔥🔥
👉By July 2025, ChatGPT has 700M+ users sending more than 2.5B+ messages per day. About 29,000 messages per second. This paper documents eight important facts about ChatGPT usage in the last three years. 63 pages of impressive statistics. To read.💙
👉Review https://t.ly/QYHSi
🦠🦠 Segment & Track Any Cell 🦠
👉RWTH unveils a novel zero-shot cell tracking framework by integrating Segment Anything 2 (SAM2) into the tracking pipeline. Source Code released💙
👉Review https://t.ly/n_srg
👉Paper https://arxiv.org/pdf/2509.09943
👉Repo https://github.com/zhuchen96/sam4celltracking
🔥21,000+ Hours Dataset🔥
👉SpatialVID is a novel large-scale video dataset with explicit spatial annotations including camera poses, depth maps, structured captions and serialized motion instructions. The dataset consists of 7,089 hours of real-world dynamic scenes. Repo & Dataset Apache-2.0 💙
👉Review https://t.ly/Y9o5k
👉Paper arxiv.org/pdf/2509.09676
👉Project nju-3dv.github.io/projects/SpatialVID/
👉Repo github.com/NJU-3DV/spatialVID
🐙Human-Centric Video Generation🐙
👉Tsinghua & #ByteDance unveil HuMo: a unified, human-centric video generation framework designed to produce HQ fine-grained, and controllable human videos from multimodal inputs. It supports strong text prompt following, consistent subject preservation, synchronized audio-driven motion. Repo released under Apache2.0💙
👉Review https://t.ly/3S8Yb
👉Paper https://arxiv.org/pdf/2509.08519
👉Project https://phantom-video.github.io/HuMo/
👉Repo https://github.com/Phantom-video/HuMo
🌱 FoMo4Wheat Foundational Model 🌱
👉PheniX Lab et al. unveil a novel family of foundational models tailored for wheat image tasks, suitable for classification, detection, counting and segmentation. Demo, Dataset, Model & Code under MIT💙
👉Review https://t.ly/UzM-Z
👉Paper arxiv.org/pdf/2509.06907
👉Project fomo4wheat.phenix-lab.com/
👉Repo github.com/PheniX-Lab/FoMo4Wheat?
👉Demo fomo4wheat.phenix-lab.com/demos
👻 From Skin to Skeleton 👻
👉This paper try unifying the SMPL body model with BSM, a new Biomechanical Skeleton Model. The SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. Model, code, and data available for research💙
👉Review https://t.ly/JsI8M
👉Paper arxiv.org/pdf/2509.06607
👉Project https://skel.is.tue.mpg.de/
🩸Foundation Red Blood Cells🩸
👉RedDino from University of Cagliari is a self-supervised foundation model designed for red blood cell (RBC) morphology analysis. Trained on 1.25M RBC images, it's the new SOTA in shape classification. Code & Models released under Apache2.0💙
👉Review https://t.ly/uWAch
👉Paper https://arxiv.org/pdf/2508.08180
👉Code https://github.com/Snarci/RedDino
👉Models huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
🖌️Real-Time Drag-Based Editing🖌️
👉The Visual AI Lab unveils Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping/inpainting. Inspired by elastic object deformation. Demo and Code released (unknown license)💙
👉Review https://t.ly/H5nlR
👉Paper https://arxiv.org/pdf/2509.04582
👉Project https://visual-ai.github.io/inpaint4drag/
👉Repo https://github.com/Visual-AI/Inpaint4Drag
👉Demo https://colab.research.google.com/drive/1fzoyNzcJNZjM1_08FE9V2V20EQxGf4PH
Friends,
I’ve just open my IG account: https://www.instagram.com/aleferra.ig | Feel free to add me
What about posting stuff about AI on IG? Thoughts?
✂️ #AI Open-Source Annotation ✂️
👉VisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for #computervision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0💙
👉Review https://t.ly/MoMvv
👉Paper https://lnkd.in/dxTncSgv
👉Repo https://lnkd.in/dCWMXp3x
✂️ #AI Open-Source Annotation ✂️
👉VisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for hashtag#computervision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0💙
👉Review https://t.ly/MoMvv
👉Paper https://lnkd.in/dxTncSgv
👉Repo https://lnkd.in/dCWMXp3x
🔥WebEyeTrack: real-time/web eye🔥
👉WebEyeTrack is a novel framework that integrates lightweight SOTA gaze estimation models directly in the browser. Bringing deep‑learning gaze estimation to the web browser and explicitly accounts for head pose. Source Code released under MIT license💙
👉Review https://t.ly/Xon9h
👉Paper https://arxiv.org/pdf/2508.19544
👉Project redforestai.github.io/WebEyeTrack/
👉Repo github.com/RedForestAi/WebEyeTrack
🍐 Promptable Human Mesh 🍐
👉PromptHMR is a promptable human pose/shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes “side information” readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. Code released under Non-Commercial Scientific Research Use Only 💙
👉Review https://t.ly/zJ7S-
👉Paper arxiv.org/pdf/2504.06397
👉Project yufu-wang.github.io/phmr-page/
👉Repo github.com/yufu-wang/PromptHMR
🐉 #DoubleDragon with #AI 🐉
👉How Double Dragon would look like in real life? Each character has been transformed with #AI to capture their style, fighting spirit, and charisma, as if they had stepped right out of the game’s streets into the real world. AUDIO ON. Damn romantic💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM
👉Post https://t.ly/0IpER
👉Channel http://www.youtube.com/@iaiaoh84
🧬 OpenVision 2 is out! 🧬
👉UCSC releases OpenVision2: a novel family of generative pretrained visual encoders that removes the text encoder and contrastive loss, training with caption-only supervision. Fully open, Apache 2.0💙
👉Review https://t.ly/Oma3w
👉Paper https://arxiv.org/pdf/2509.01644
👉Project https://ucsc-vlaa.github.io/OpenVision2/
👉Repo https://github.com/UCSC-VLAA/OpenVision
