⚡️Feature Matching at Light Speed⚡️ 
 
👉LightGlue is a lightweight feature matcher with high accuracy and blazing fast inference
 
😎Review https://t.ly/jkecX
😎Paper arxiv.org/pdf/2306.13643.pdf
😎Code github.com/cvg/LightGlue
👉LightGlue is a lightweight feature matcher with high accuracy and blazing fast inference
😎Review https://t.ly/jkecX
😎Paper arxiv.org/pdf/2306.13643.pdf
😎Code github.com/cvg/LightGlue
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  🕹️ CoDeF: Video Content Deformation Fields 🕹️ 
 
👉CoDeF is a new type of video representation for video-editing tasks
 
😎Review https://t.ly/PIVl-
😎Paper arxiv.org/pdf/2308.07926.pdf
😎Project https://qiuyu96.github.io/CoDeF
😎Code https://github.com/qiuyu96/CoDeF
👉CoDeF is a new type of video representation for video-editing tasks
😎Review https://t.ly/PIVl-
😎Paper arxiv.org/pdf/2308.07926.pdf
😎Project https://qiuyu96.github.io/CoDeF
😎Code https://github.com/qiuyu96/CoDeF
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  Hello everybody,
a lot of you asked me to open the comments to better enjoy the posts. I want to follow your suggestion, hope you will enjoy this new mood!
🔥 NO SPAM
🔥 NO COMMERCIAL
🔥 NO UNRESPECTFUL MESSAGEs
 
🧡JUST AI & SCIENCE
 
⚠️ BAN AT THE FIRST VIOLATION ⚠️
a lot of you asked me to open the comments to better enjoy the posts. I want to follow your suggestion, hope you will enjoy this new mood!
🔥 NO SPAM
🔥 NO COMMERCIAL
🔥 NO UNRESPECTFUL MESSAGEs
🧡JUST AI & SCIENCE
⚠️ BAN AT THE FIRST VIOLATION ⚠️
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  AI with Papers - Artificial Intelligence & Deep Learning pinned «Hello everybody, a lot of you asked me to open the comments to better enjoy the posts. I want to follow your suggestion, hope you will enjoy this new mood!   🔥 NO SPAM  🔥 NO COMMERCIAL  🔥 NO UNRESPECTFUL MESSAGEs    🧡JUST AI & SCIENCE     ⚠️ BAN AT THE FIRST…»
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  🦠 Instance-Level Semantics of Cells 🦠 
 
👉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
👉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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  🌵POCO: 3D HPS + Confidence🌵 
 
👉 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
👉 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: 360◦ 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
👉#Toyota (+GIT) unveils NeO360: 360◦ 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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  🥕  Scenimefy: I-2-I for anime  🥕 
 
👉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
👉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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  🐨  Watch Your Steps: Editing by Text  🐨 
 
👉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
👉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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  💡 Relighting NeRF 💡 
  
👉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
👉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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  🪶  ReST: Multi-Camera MOT 🪶 
 
👉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
👉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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  🌲MagicEdit: Magic Video Edit🌲  
   
👉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
👉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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  ✂️ VideoCutLER: Simple UVIS ✂️ 
 
👉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
👉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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  🐦 3D Pigeons Pose & Tracking 🐦 
 
👉 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/
👉 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
👉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.iss.onetademolab.com/
👉#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.iss.onetademolab.com/
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  ♊️ Doppelgangers in Structures ♊️
👉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/
👉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
👉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
👉 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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  🔥🔥 #META's DINOv2 is now commercial! 🔥🔥 
  
👉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/
👉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
👉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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