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
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 17 259 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 497-o'rinni va Malayziya mintaqasida 2 130-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 259 obunachiga ega bo‘ldi.
21 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 260 ga, so‘nggi 24 soatda esa 6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 20.25% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 7.12% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 496 marta ko‘riladi; birinchi sutkada odatda 1 229 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 25 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 22 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.
17 259
Obunachilar
+624 soatlar
+97 kun
+26030 kun
Postlar arxiv
🍿PanoSeg3R: SOTA 3D Segmentation🍿
👉PanoSeg3R is a novel feed-forward framework for 3D panoramic semantic segmentation. New SOTA. Code coming💙
👉Review https://lnkd.in/p/eKCKWv3g
👉Paper https://arxiv.org/pdf/2609.22687
👉Project https://harryyoon777.github.io/PanoSeg3R/#
👉Repo TBA
🔥Agentic Image-to-Scene🔥
👉HARMONY by UPenn is a hierarchical chain-of-thought framework that leverages both agentic reasoning and visual geometry foundation. Impressive 3D scenes. Repo TBA💙
👉Review https://lnkd.in/p/ep2hmRSp
👉Paper https://arxiv.org/pdf/2609.26793
👉Data https://huggingface.co/datasets/ShufanSun/harmony
👉Project https://cwchenwang.github.io/harmony/
👉Repo TBA
💦SOTA Splashing Liquids💦
👉SplashSplat reconstructs splashing liquids from real multi-view vide. Impose physical structure only where the observations can constrain it. Impressive results, SOTA. Code TBR under MIT💙
👉Review https://lnkd.in/p/euv4eBja
👉Paper https://arxiv.org/pdf/2609.20818
👉Project niko-creater.github.io/splashsplat-web/
👉Repo https://github.com/Niko-creater/Splashsplat
👋 EventEgoHands++ is out! 👋
👉EventEgoHands++ is a novel framework for event-based 3D hand mesh reconstruction from an egocentric viewpoint. 1M+ samples dataset! Code/Data released💙
👉Review https://lnkd.in/p/eTbPvXbW
👉Paper https://arxiv.org/pdf/2609.17189
👉Repo https://github.com/ryhara/EventEgoHandsV2
👉Project https://ryhara.github.io/EventEgoHandsV2/
🔥 RelateAnything is gold! 🔥
👉RelateAnything is a 53M-parameter relation model that takes an image and a set of regions from any source and returns scored relations over a predicate vocabulary supplied at inference as a list of strings. Impressive results. Repo under Apache 2.0💙
👉Review https://lnkd.in/p/etAcdFM3
👉Paper https://arxiv.org/pdf/2609.12552
👉Repo https://github.com/Maelic/RelateAnything
👉Project https://maelic.github.io/RelateAnythingProject/
🦺Efficient/Scalable Video Pretraining🦺
👉LeVJEPA1 (Yann Lecun) is the first video encoder trained under LeJEPA’s collapse-free objective, and evaluate it under frozen probing against video and image pretraining baselines retrained on identical data, in both epoch-matched and FLOP-matched regimes. Repo under MIT💙
👉Review https://lnkd.in/p/eJQAm3AN
👉Paper https://lnkd.in/eCzzTiNH
👉Project https://levjepa.github.io/
👉Repo https://lnkd.in/etiF5CDj
🔥🔥 Marigold V2 is out 🔥🔥
👉Marigold V2 is out: depth, (impressive) see-through depth, surface normals, albedo, and other dense modalities. SOTA results. Repo under Apache 2.0💙
#AI #deeplearning #AIwithPapers
👉Review https://lnkd.in/p/eKM44yDQ
👉Paper https://arxiv.org/pdf/2609.08084
👉Repo https://github.com/huawei-bayerlab/marigold-v2
👉Project https://huggingface.co/spaces/huawei-bayerlab/marigold-v2-web
🏀McByte++ tracking-by-detection🏀
👉McByte++ is the newer extension of McByte that advances training-free sports MOT toward long-term ID tracking, while simultaneously improving efficiency and runtime performance. Repo under Apache 2.0💙
👉Review https://lnkd.in/p/e4-diVJS
👉Paper https://lnkd.in/e_Vxky-b
👉Repo https://lnkd.in/e8SeCYmk
🔥 #AIwithPapers: we are 17,000+ 🔥
👉 Even though 100+ bots are trying to join the discussion chats every day, there are 17,000 of us! Almost all of us are still humans 🧟
😈 Invite -> https://t.me/AI_DeepLearning
👻Emerging Objs from Motion👻
👉Motion boundaries provide a strong signal for object-level grouping and can be used to derive pseudo-instance supervision. Suitable for: mono-depth, 3D object detection, 3D occupancy, and end-to-end planning. Repo under Apache 2.0💙
👉Review https://lnkd.in/p/eezZrSJE
👉Paper https://arxiv.org/pdf/2609.04348
👉Project https://tj12342.github.io/object-concepts-from-motion/
👉Repo https://github.com/TJ12342/object-concepts-from-motion/tree/main
+++ Mistral raises 3B € +++
👉Discussion: https://lnkd.in/p/eVpF--VW
🪣Weather-Conditioned Depth Anything🪣
👉Weather-Conditioned Depth Anything from Texas A&M is the new SOTA in weather-robust depth estimation. A curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. Repo under Apache💙
👉Review https://lnkd.in/p/eW-dsepD
👉Paper https://lnkd.in/er_MvVft
👉Project https://lnkd.in/ehXPs3C7
👉Repo https://lnkd.in/edk7Ts_r
🍚Vision Weight Estimation🍚
👉Doppio is a novel video dataset capturing video of falling ground coffee, paired with precise, per-frame ground-truth weight measurements: OCR readings are extracted from the display, smoothed and time-lag compensated, and paired with per-frame weight annotations. Repo to be released under Apache💙
👉Review https://www.linkedin.com/posts/visionarynet_computer-vision-weight-estimation-activity-7501900695457951744-ArBO
👉Paper https://lnkd.in/eHuy87SX
👉Project https://lnkd.in/e9g9zeK3
👉Repo https://lnkd.in/emUePTiq
🦑Unified Segmentation n' Retrieval🦑
👉FoundYou gets an example of your object and it segments the same physical instance in a new image or retrieve it from a large gallery with ONE super-compact model. Repo/demo available💙
👉Review https://lnkd.in/p/ex2qnKHW
👉Paper arxiv.org/pdf/2608.29917
👉Project https://lnkd.in/eNEUB_nV
👉Repo https://lnkd.in/eRyDY6Ue
🍋🟩Remesh-Aware Mesh Deformation🍋🟩
👉RADmesh is a novel generative deformation technique enhanced by remeshing. Given a text prompt, it deforms and remeshes a mesh region to form new geometric features. Repo MIT💙
👉Review https://lnkd.in/p/eK6FZv9c
👉Paper https://arxiv.org/pdf/2608.17182
👉Project https://threedle.github.io/radmesh/
👉Repo https://github.com/threedle/radmesh/
🐆 Anyone in 4D is out 🐆
👉4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstruction. Full repo under Apache 2.0💙
👉Review https://lnkd.in/p/ec4dzGvb
👉Paper https://arxiv.org/pdf/2608.20335
👉Project https://4danyone.github.io
👉Repo github.com/ant-research/4DAnyone
🔥🔥UPAL: Unified Points n' Lines🔥🔥
👉ETH (+Microsoft Spatial AI Lab) unveils a novel feature extractor that jointly extracts keypoints, lines, and feature descriptors within a single lightweight net. SOTA in line detection can be achieved by adding only three convolutional layers to existing point extractor. Repo under Apache💙
👉Review https://lnkd.in/p/eW8j5JZj
👉Paper https://arxiv.org/pdf/2608.19894
👉Repo https://github.com/francois141/upal
🐠Dual-branch Elasticity ID-Tracking🐠
👉TIDE: tracking dense, homogeneous targets, providing a scalable dual-branch design to accommodate diverse hardware constraints. MIT license💙
👉Review https://t.ly/WEDeY
👉Paper https://arxiv.org/pdf/2607.26412
👉Project https://vranlee.github.io/TIDE/
👉Repo https://github.com/vranlee/TIDE
🔥Decoder-only Any-to-Any Model🔥
👉MODUS unifies any-to-any multimodal generation with one decoder, two experts, and zero task heads. Impressive work. Repo under Apache💙
👉Review https://t.ly/-2QKT
👉Paper https://lnkd.in/dhfBQGhB
👉Project https://lnkd.in/dPD_ECXk
👉Repo https://lnkd.in/dbDHw24u
