AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning, #MachineLearning, LLMs and #ComputerVision

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/

#artificialintelligence #machinelearning #ml #AI
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πŸ‘’Generative View Stitching πŸ‘’

πŸ‘‰GVS is a novel approach that enables collision-free camera-guided video generation for predefined trajectories, it's a non-autoregressive alternative to video length extrapolation. Full repo under MITπŸ’™

πŸ‘‰Review https://t.ly/TiN_5
πŸ‘‰Paper https://arxiv.org/pdf/2510.24718
πŸ‘‰Project https://andrewsonga.github.io/gvs/
πŸ‘‰Repo github.com/andrewsonga/generative_view_stitching
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Greetings from the SMART CITY WORLD CONGRESS in Barcellona. If you are around, ping me ;)
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πŸ”ͺTracking Object TransformationsπŸ”ͺ

πŸ‘‰"Track Any State": tracking objects through transformations while detecting/describing state changes. Repo & Dataset available under MITπŸ’™

πŸ‘‰Review https://t.ly/NPyW4
πŸ‘‰Paper https://lnkd.in/d4pA3bXJ
πŸ‘‰Project https://lnkd.in/dgbNfCuj
πŸ‘‰Repo https://lnkd.in/dtVWq2z7
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πŸ”₯πŸ”₯ Sunday mood πŸ”₯πŸ”₯
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🎸Another BRIXEL in the Wall 🎸

πŸ‘‰BRIXEL allows the user to produce high-resolution feature maps using the DINOv3 backbone without requiring large amounts of compute. Repo releasedπŸ’™

πŸ‘‰Review https://t.ly/fZPwC
πŸ‘‰Paper arxiv.org/pdf/2511.05168
πŸ‘‰Repo github.com/alexanderlappe/BRIXEL
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🐼Pixel-Dense Embedding🐼

πŸ‘‰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 πŸ’™

πŸ‘‰Review https://t.ly/aUx_U
πŸ‘‰Paper arxiv.org/pdf/2511.07696
πŸ‘‰Project tum-vision.github.io/flowfeat
πŸ‘‰Repo github.com/tum-vision/flowfeat
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🚨 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.

πŸ‘‰ Starting today, I’ll be removing these connections from LinkedIn and banning such individuals from this group.

πŸ“’ 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.
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🟩 Foundational Humanoid 🟩

πŸ‘‰#NVIDIA unveils SONIC a novel foundational model for high-precision teleoperation & interactive control capabilities (running, jumping, crawling) with natural human-like movements. Code announcedπŸ’™

πŸ‘‰Review https://t.ly/_3wnt
πŸ‘‰Paper https://lnkd.in/dctfShu8
πŸ‘‰Project https://lnkd.in/d_inmA2p
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πŸ”₯Depth Anything 3 is outπŸ”₯

πŸ‘‰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 2.0πŸ’™

πŸ‘‰Review https://t.ly/AOPu7
πŸ‘‰Paper arxiv.org/pdf/2511.10647
πŸ‘‰Project https://lnkd.in/dnByyn2z
πŸ‘‰Repo https://lnkd.in/daCVz_4a
πŸ‘‰Demo https://lnkd.in/dKUZiJt
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🌩️ It's "Time-to-Move" 🌩️

πŸ‘‰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!

πŸ‘‰Review https://t.ly/0pwXm
πŸ‘‰Paper https://lnkd.in/dxD3uHYb
πŸ‘‰Project https://lnkd.in/dcE5juyM
πŸ‘‰Repo https://lnkd.in/dMMUjybJ
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⌚ Multi-Shot Video Segmentation ⌚

πŸ‘‰Fudan focuses on an underexplored task of multi-shot video object segmentation (MVOS). Benchmark and repo available (the extension part of SAM) under Apache 2.0πŸ’™

πŸ‘‰Review https://t.ly/WBW00
πŸ‘‰Paper https://arxiv.org/pdf/2511.13715
πŸ‘‰Project https://henghuiding.com/SAAS/
πŸ‘‰Repo https://github.com/FudanCVL/SAAS
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πŸ”₯ SAM 3/3D are OUT!! πŸ”₯

πŸ‘‰#META released SAM 3, a unified model for detection, segmentation, tracking of objects in images & video using text, exemplar & visual prompts. Repo/Models under proprietary licenseπŸ’™

πŸ‘‰Review https://t.ly/lnRZN
πŸ‘‰Paper https://t.ly/5tq9N
πŸ‘‰Project https://ai.meta.com/sam3/
πŸ‘‰Demo: https://segment-anything.com
πŸ‘‰Repo https://github.com/facebookresearch/sam3
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🍯Unwrapping of 3D Meshes🍯

πŸ‘‰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 releasedπŸ’™

πŸ‘‰Review https://t.ly/8dNIY
πŸ‘‰Paper arxiv.org/pdf/2511.16659
πŸ‘‰Project www.zhaoningwang.com/PartUV/
πŸ‘‰Repo github.com/EricWang12/PartUV
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πŸ• Upsample Anything πŸ•

πŸ‘‰Upsample Anything, a novel universal, training-free up-sampler via lightweight test-time optimization. No code but it's a relevant paperπŸ’™

πŸ‘‰Review https://t.ly/7LE6G
πŸ‘‰Paper https://lnkd.in/dsUfdtih
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🦞Single Synthetic Image per Class🦞

πŸ‘‰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 availableπŸ’™

πŸ‘‰Review https://t.ly/dD3un
πŸ‘‰Paper arxiv.org/pdf/2511.16674
πŸ‘‰Project linear-gradient-matching.github.io/
πŸ‘‰Repo github.com/GeorgeCazenavette/linear-gradient-matching
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πŸ§ͺ EfficientSAM3 is out πŸ§ͺ

πŸ‘‰Bristol announces EfficientSAM3, a family of efficient models built on Progressive Hierarchical Distillation that transfers capability from SAM3 to lightweight students. Code coming (in sync with SAM3 release)πŸ’™

πŸ‘‰Review https://t.ly/bfXP2
πŸ‘‰Paper arxiv.org/pdf/2511.15833
πŸ‘‰Project simonzeng7108.github.io/efficientsam3/
πŸ‘‰Repo github.com/SimonZeng7108/efficientsam3
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🌩️ Cloud4D in time 🌩️

πŸ‘‰Cloud4D: physically-realistic 3D cloud fields using ground-based cameras at a 25 m spatial resolution and 5 s temporal resolution. Repo coming, Data releasedπŸ’™

πŸ‘‰Review https://t.ly/w7Zly
πŸ‘‰Paper arxiv.org/pdf/2511.19431
πŸ‘‰Project cloud4d.jacob-lin.com/
πŸ‘‰Data https://drive.google.com/drive/folders/1QU_0kIUXIVt8h3uqygBeaF3Gvr_L5SdX?usp=drive_link
πŸ‘‰Repo TBA
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