AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates on Deep Learning, Machine Learning, and Computer Vision (with Papers).

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
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ðŸĶ  Instance-Level Semantics of Cells ðŸĶ 

👉TYC: novel dataset for understanding instance-level semantics & motions of cells in microstructures

😎Review https://t.ly/y-4VZ
😎Paper arxiv.org/pdf/2308.12116.pdf
😎Project christophreich1996.github.io/tyc_dataset/
😎Code github.com/ChristophReich1996/TYC-Dataset
😎Data tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3930
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ðŸŒĩPOCO: 3D HPS + ConfidenceðŸŒĩ

👉 Novel framework for HPS: #3D human body + confidence in a single feed-forward pass

😎Review https://t.ly/cDePe
😎Paper arxiv.org/pdf/2308.12965.pdf
😎Project https://poco.is.tue.mpg.de
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🌆 NeO360: NeRF for Sparse Outdoor 🌆

👉#Toyota (+GIT) unveils NeO360: 360â—Ķ outdoor scenes from a single or a few posed RGB images

😎Review https://t.ly/JDJZg
😎Paper arxiv.org/pdf/2308.12967.pdf
😎Project zubair-irshad.github.io/projects/neo360.html
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ðŸĨ• Scenimefy: I-2-I for anime ðŸĨ•

👉S-Lab unveils a novel semi-supervised I-2-I translation framework + HD dataset for anime

😎Review https://t.ly/IsdEG
😎Paper arxiv.org/pdf/2308.12968.pdf
😎Code https://github.com/Yuxinn-J/Scenimefy
😎Project https://yuxinn-j.github.io/projects/Scenimefy.html
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ðŸĻ Watch Your Steps: Editing by Text ðŸĻ

👉The novel SOTA in image & scene (text) editing via denoising diffusion models

😎Review https://t.ly/fv9wn
😎Paper arxiv.org/pdf/2308.08947.pdf
😎Project ashmrz.github.io/WatchYourSteps
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ðŸ’Ą Relighting NeRF ðŸ’Ą

👉Neural implicit radiance representation for free viewpoint relighting of an object lit by a moving point light

😎Review https://t.ly/J-3_L
😎Project nrhints.github.io
😎Code github.com/iamNCJ/NRHints
😎Paper nrhints.github.io/pdfs/nrhints-sig23.pdf
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ðŸŠķ ReST: Multi-Camera MOT ðŸŠķ

👉Novel reconfigurable two-steps graph model for multi-camera multi object video tracking (MC-MOT)

😎Review https://t.ly/3C5tb
😎Paper arxiv.org/pdf/2308.13229.pdf
😎Code github.com/chengche6230/ReST
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ðŸŒēMagicEdit: Magic Video EditðŸŒē

👉MagicEdit: explicit disentangling content, structure & motion for Hi-Fi and temporally coherent video editing

😎Report https://t.ly/tREX4
😎Paper arxiv.org/pdf/2308.14749.pdf
😎Project magic-edit.github.io
😎Code github.com/magic-research/magic-edit
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✂ïļ VideoCutLER: Simple UVIS ✂ïļ

👉VideoCutLER is a simple unsupervised video instance segmentation (UVIS) method without relying on optical flows

😎Review https://t.ly/PBBjG
😎Paper arxiv.org/pdf/2308.14710.pdf
😎Project people.eecs.berkeley.edu/~xdwang/projects/CutLER
😎Code github.com/facebookresearch/CutLER/tree/main/videocutler
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ðŸĶ 3D Pigeons Pose & Tracking ðŸĶ

👉 3D-MuPPET: estimate and track 3D poses of pigeons with multiple-views

😎Review https://t.ly/jfAJJ
😎Paper arxiv.org/pdf/2308.15316.pdf
😎Code github.com/alexhang212/3D-MuPPET/
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🎍RoboTAP: Dense Tracking for Few-Shot Imitation🎍

👉RoboTAP: novel dense tracking representation for robotic arm

😎Review https://t.ly/MCO_V
😎Paper arxiv.org/pdf/2308.15975.pdf
😎Project https://robotap.github.io/
😎Code github.com/deepmind/tapnet
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⛹FACET: Fairness in Computer Vision⛹

👉#META AI opens a large, publicly available dataset for classification, detection & segmentation. Potential performance disparities & challenges across sensitive demographic attributes

😎Review https://t.ly/mKn-t
😎Paper arxiv.org/pdf/2309.00035.pdf
😎Dataset https://facet.metademolab.com/
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♊ïļ Doppelgangers in Structures ♊ïļ

👉A novel learning-based approach for visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions

😎Review https://t.ly/9yLot
😎Paper arxiv.org/pdf/2309.02420.pdf
😎Code github.com/RuojinCai/Doppelgangers
😎Project doppelgangers-3d.github.io/
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🍃 Tracking Anything with Decoupled VOS 🍃

👉A novel VOS approach that extends SAM for open-world video segmentation with no user input required

😎Review https://t.ly/xeobR
😎Paper arxiv.org/pdf/2309.03903.pdf
😎Project hkchengrex.com/Tracking-Anything-with-DEVA
😎Code github.com/hkchengrex/Tracking-Anything-with-DEVA
😎Colab https://colab.research.google.com/drive/1OsyNVoV_7ETD1zIE8UWxL3NXxu12m_YZ
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🊷 Diffusive Consistent Video Editing 🊷

👉 Weizmann Institute of Science unveils TokenFlow, a novel text-to-image diffusion model for text-driven video editing

😎Review https://t.ly/ru8km
😎Paper arxiv.org/pdf/2307.10373.pdf
😎Project diffusion-tokenflow.github.io
😎Code github.com/omerbt/TokenFlow
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ðŸ”ĨðŸ”Ĩ #META's DINOv2 is now commercial! ðŸ”ĨðŸ”Ĩ

👉Universal features for image classification, instance retrieval, video understanding, depth & semantic segmentation. Now suitable for commercial.

😎Review https://t.ly/LNrGy
😎Paper arxiv.org/pdf/2304.07193.pdf
😎Code github.com/facebookresearch/dinov2
😎Demo dinov2.metademolab.com/
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🧄FreeMan: towards #3D Humans 🧄

👉FreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!

😎Review https://t.ly/ICxpA
😎Paper arxiv.org/pdf/2309.05073.pdf
😎Project wangjiongw.github.io/freeman
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ðŸĶŠ MagiCapture: HD Multi-Concept Portrait ðŸĶŠ

👉KAIST unveils MagiCapture: integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references

😎Review https://t.ly/c9rOo
😎Paper https://arxiv.org/pdf/2309.06895.pdf
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âš― Dynamic NeRFs for Soccer âš―

👉SoccerNeRF: first attempt of "cheap" NeRF applied to football for reconstructing soccer replays in space and time.

😎Review https://t.ly/Ywcvk
😎Paper arxiv.org/pdf/2309.06802.pdf
😎Project https://soccernerfs.isach.be/
😎Code github.com/iSach/SoccerNeRFs
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â˜Ēïļ GlueStick: Graph Neural Matching â˜Ēïļ

👉GlueStick is joint deep matcher for points and lines that leverages the connectivity information between nodes to better glue them together

😎Review https://t.ly/Atxqo
😎Paper arxiv.org/pdf/2304.02008.pdf
😎Code https://github.com/cvg/GlueStick
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