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 17 262 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
🌵Instance-Level Video Generation🌵
👉InstanceV is the first video generation framework to be designed specifically for instance-level control at the architectural level. Code & Data announced💙
👉Review https://t.ly/y_TBT
👉Paper arxiv.org/pdf/2511.23146
👉Project aliothchen.github.io/projects/InstanceV/
👉Repo TBA
🕶️ Seeing without Pixels 🕶️
👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙
👉Review https://t.ly/Ymd1c
👉Paper arxiv.org/pdf/2511.21681
👉Project sites.google.com/view/seeing-without-pixels
🕶️ Seeing without Pixels 🕶️
👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? #Google Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅CamFormer: pre-trained dedicated trajectory encoder
✅Proposing a novel contextualized trajectory encoding
✅Scenarios: egocentric & exocentric (third-person)
✅Outperforming "heavy" vision models in key scenarios
#artificialintelligence #AI #deeplearning #AIwithPapers #LLM
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2511.21681
👉Project https://sites.google.com/view/seeing-without-pixels
🔥 Smell Like Vision Spirit 🔥
👉New York Smells is a novel large-scale dataset of paired vision and olfaction captured in-the-wild, enabling the new task of cross-modal learning between smell and sight. With the lights out, it's less dangerous. Dataset available💙
👉Review https://t.ly/Ycn_B
👉Paper arxiv.org/pdf/2511.20544
👉Project smell.cs.columbia.edu/
🍓MotionV2V: Editing Motion in Video🍓
👉 Google unveils motion edits, a new approach for editing videos by controlling the change in motion from the original to the edited video using diffusion models. Impressive results. Repo released soon💙
👉Review https://t.ly/s0sIT
👉Paper https://arxiv.org/pdf/2511.20640
👉Project https://ryanndagreat.github.io/MotionV2V/
👉Repo https://github.com/RyannDaGreat/MotionV2V
🌩️ Cloud4D in time 🌩️
👉Cloud4D: physically-realistic 3D cloud fields using ground-based cameras at a 25 m spatial resolution and 5 s temporal resolution. Repo coming, Data released💙
👉Review https://t.ly/w7Zly
👉Paper arxiv.org/pdf/2511.19431
👉Project cloud4d.jacob-lin.com/
👉Repo TBD
👉Data https://drive.google.com/drive/folders/1QU_0kIUXIVt8h3uqygBeaF3Gvr_L5SdX?usp=drive_link
🧪EfficientSAM3 is out 🧪
👉Bristol announces EfficientSAM3, a family of efficient models built on Progressive Hierarchical Distillation (PHD) that transfers capability from SAM3 to lightweight students. Code coming (in sync with SAM3 release)💙
👉Review https://t.ly/bfXP2
👉Paper https://arxiv.org/pdf/2511.15833
👉Project https://simonzeng7108.github.io/efficientsam3/
👉Repo https://github.com/SimonZeng7108/efficientsam3
🦞Single Synthetic Image per Class🦞
👉MIT unveils Linear Gradient Matching (H/T Torralba), a novel method of distillation to use a single synthetic image per class for linear classifiers training (and more). Repo available💙
👉Review https://t.ly/dD3un
👉Paper arxiv.org/pdf/2511.16674
👉Project linear-gradient-matching.github.io/
👉Repo github.com/GeorgeCazenavette/linear-gradient-matching
🍕 Upsample Anything 🍕
👉Upsample Anything, a novel universal, training-free up-sampler via lightweight test-time optimization. No code but it's a relevant paper💙
👉Review https://t.ly/7LE6G
👉Paper https://lnkd.in/dsUfdtih
🍯Unwrapping of 3D Meshes🍯
👉PartUV is a novel part-based UV unwrapping method for 3D meshes; it combines learned part priors with geometric cues to generate a compact set of part-aligned charts. Repo released💙
👉Review https://t.ly/8dNIY
👉Paper arxiv.org/pdf/2511.16659
👉Project www.zhaoningwang.com/PartUV/
👉Repo github.com/EricWang12/PartUV
🔥 SAM 3/3D are OUT!! 🔥
👉#META released the SAM 3 family, a unified model for detection, segmentation, and tracking of objects in images & video using text, exemplar & visual prompts. Repo/Models under proprietary SAM license💙
👉Review https://t.ly/lnRZN
👉Paper https://t.ly/5tq9N
👉Project https://ai.meta.com/sam3/
👉Demo: https://segment-anything.com
👉Repo https://github.com/facebookresearch/sam3
⌚ Multi-Shot Video Segmentation ⌚
👉Fudan focuses on an underexplored task of multi-shot video object segmentation (MVOS). Benchmark and repo available (the extension part of SAM) under Apache 2.0💙
👉Review https://t.ly/WBW00
👉Paper https://arxiv.org/pdf/2511.13715
👉Project https://henghuiding.com/SAAS/
👉Repo https://github.com/FudanCVL/SAAS
🌩️ It's "Time-to-Move" 🌩️
👉Technion + Nvidia Time-to-Move (TTM) is a training-free, plug-and-play framework for motion- and appearance-controlled video generation with I2V diffusion models (Wan 2.2, CogVideoX, & Stable VD). Impressive results!
👉Review https://t.ly/0pwXm
👉Paper https://lnkd.in/dxD3uHYb
👉Project https://lnkd.in/dcE5juyM
👉Repo https://lnkd.in/dMMUjybJ
🔥Depth Anything 3 is out🔥
👉#ByteDance unveils Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from arbitrary visual inputs, with or without known camera poses. Repo under Apache 2.0💙
👉Review https://t.ly/AOPu7
👉Paper arxiv.org/pdf/2511.10647
👉Project https://lnkd.in/dnByyn2z
👉Repo https://lnkd.in/daCVz_4a
👉Demo https://lnkd.in/dKUZiJt
🟩 Foundational Humanoid 🟩
👉#NVIDIA unveils SONIC a novel foundational model for high-precision teleoperation & interactive control capabilities (running, jumping, crawling) with natural human-like movements. Code announced💙
👉Review https://t.ly/aUx_U
👉Paper https://lnkd.in/dctfShu8
👉Project https://lnkd.in/d_inmA2p
🚨 Announcement 🚨
I’ve received numerous reports of people blatantly copying my content on LinkedIn just to get a few likes.
Let me be very clear: I put a great deal of time and effort into reviewing papers and creating original, meaningful content. It’s disappointing to see professionals (some of whom are even members of this group or my connections) resorting to plagiarism instead of contributing their own ideas.
👉 Starting today, I’ll be removing these connections from LinkedIn and banning such individuals from this group.
📢 I also encourage everyone to report these cases whenever you come across them. Every single report helps stop this bad habit and keeps our community fair, respectful, and authentic.
🐼Pixel-Dense Embedding of Motion🐼
👉FlowFeat is a novel high-resolution and multi-task feature representation that embeds a distribution of plausible apparent motions, or motion profiles. Repo available under 💙
#artificialintelligence #AI #deeplearning #AIwithPapers
👉Review https://t.ly/aUx_U
👉Paper arxiv.org/pdf/2511.07696
👉Project tum-vision.github.io/flowfeat
👉Repo github.com/tum-vision/flowfeat
🎸Another BRIXEL in the Wall 🎸
👉BRIXEL allows the user to produce high-resolution feature maps using the DINOv3 backbone without requiring large amounts of compute. Repo released💙
👉Review https://t.ly/fZPwC
👉Paper arxiv.org/pdf/2511.05168
👉Repo github.com/alexanderlappe/BRIXEL
