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 17 262 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 491-o'rinni va Malayziya mintaqasida 2 128-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 262 obunachiga ega bo‘ldi.
23 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 255 ga, so‘nggi 24 soatda esa 2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 18.93% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 7.17% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 267 marta ko‘riladi; birinchi sutkada odatda 1 237 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 20 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 24 Oktabr, 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.
17 262
Obunachilar
+224 soatlar
+67 kun
+25530 kun
Postlar arxiv
👽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
