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Released at
#CVPR2019, MediaPipe is Google's new framework for media processing pipelines, combining model-based inference via TensorFlow with traditional CV tasks like optical flow, pose tracking, and more. Used in existing projects like Motion Stills. https://sites.google.com/view/perception-cv4arvr/mediapipe …pic.twitter.com/js2S3qu750 -
Now available for commercial use on NGC, pix2pixHD developed by
@NVIDIA researchers generates high-resolution photorealistic images from high-level labels.#CVPR2019 https://ngc.nvidia.com/catalog/models/nvidia:pix2pixhd … -
When video description meets bounding boxes! We are now releasing data/code/models/leaderboard (yes, everything) on our
#CVPR2019 oral paper Grounded Video Description: Paper: https://lnkd.in/g-fs79K Dataset (158k bboxes on 52k captions):https://lnkd.in/g-pSKzfPrikaži ovu nit -
If you're at
#CVPR2019 kindly visit me present our work on "Cycle Consistency for robust VQA" in Grand Ballroom at 14.10. My@facebookai co-authors will be there to answer all your questions in the poster session #184 that follows. Dataset + Code + Paper:https://facebookresearch.github.io/VQA-Rephrasings/ … -
Ending my
#CVPR2019 conference as a Crazy Rich Bayesian! Go Bayesians! pic.twitter.com/NEq9qpkaTX
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If you enjoy
AND realtime visual-inertial SLAM, @Occipital is hosting a happy hour tomorrow at 5pm. DM me for details!#CVPR2019 pic.twitter.com/vmLD5HZ9eNPrikaži ovu nit -
I liked the DeepMapping paper from Ding and Feng at
#CVPR2019. A bit similar to DIP, they use deep learning machinery to solve a surprising optimisation problem (no learning on a dataset): pose graph alignment for a set of pose scans. https://arxiv.org/abs/1811.11397@czarnowskijpic.twitter.com/mOBLwslMwx -
Best paper award at
#CVPR2019 main idea: seeing around the corner at non-line-of-sight (NLOS) objects by using Fermat paths, which is a new theory of how NLOS photons follow specific geometric paths. http://imaging.cs.cmu.edu/fermat_paths/assets/cvpr2019.pdf …pic.twitter.com/IMj1E4fnYs
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We have a fantastic representation at
#CVPR2019 this week!

Come and say 'HI' to Marco, Lewis, Jamie, Simon, Miroslaw, Josef, John, Armin, Adrian, Tao, and Yi-Zhe! https://bit.ly/2KXdvQ2 https://bit.ly/31GpJCy @UniOfSurrey

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If you have any doubts about the applicability of CNNs to local feature detection for robust pose estimation, come to our Deep Charuco poster today at
#CVPR2019 . This videos shows a shadow effect and traditional methods are simply too frail. Red=fail http://charuco.net pic.twitter.com/31vfkGu9GX -
Deep Flow-Guided Video Inpainting
#CVPR2019 By@ccloy Completing a missing flow is easier than filling in pixels of a missing region directly. SoTA on DAVIS and YouTube-VOS Code https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting … ArXiv https://arxiv.org/abs/1905.02884 pic.twitter.com/Qyj28rXThu -
Learn about Learning from Unlabeled Videos at
#CVPR2019, Sunday in Room E, 9:00am Fresh posters and keynotes: Antonio Torralba, Noah Snavely, Andrew Zisserman, Bill Freeman, Abhinav Gupta, Kristen Grauman https://sites.google.com/view/luv2019/home?authuser=0 …Prikaži ovu nit -
We willl be presenting our work "Divide and Conquer the Embedding Space for Metric Learning" at
#CVPR2019 on Tuesday 18th: Poster 24 at 10:15. Authors: Me, Vadim Tschernezki, Uta Büchler (@uta0590) and Björn Ommer. Paper and Code: https://bit.ly/dcesml pic.twitter.com/Tw1X40FV27
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Y. Niitani of Preferred Networks gave an oral presentation at
#CVPR2019 "Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects" used to win 2nd prize @ Google AI Open Images challenge (w/ Akiba, Kerola, Ogawa, Sano & Suzuki) http://openaccess.thecvf.com/content_CVPR_2019/papers/Niitani_Sampling_Techniques_for_Large-Scale_Object_Detection_From_Sparsely_Annotated_Objects_CVPR_2019_paper.pdf … pic.twitter.com/jbQjC7BO4u
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For those interested in some of the latest self-driving research (especially those participating in our
@Kaggle competition), we’ve collected our favorite self-driving content from#CVPR2019 in our latest reader's digest. Read them here: https://medium.com/lyftlevel5/cvpr-digest-9195adbd5d0c … -
Pleased to now also share the code for our
#CVPR2019 paper: "Meta-learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification with the CODEBRIM Dataset". Code: https://bit.ly/2XtCT3c Paper: https://bit.ly/2UNASSv Dataset: https://zenodo.org/record/2620293 pic.twitter.com/IYI3cZ0UxX
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All papers with code at
#CVPR2019 https://paperswithcode.com/conference/cvpr-2019-6 … -
We're launching a http://valeo.ai blog for sharing our latest work and reflections on research for safer and increasingly autonomous vehicles. The first post describes our recent
#CVPR2019 paper ADVENT for unsupervised domain adaptation: https://medium.com/@valeo.ai/advent-adversarial-entropy-minimization-for-domain-adaptation-in-semantic-segmentation-dba21934430b … -
NVIDIA Research will present 20 papers at
#CVPR2019, including 11 orals. Full list is here: https://nvda.ws/31xiZab . Check it out!pic.twitter.com/p39jHx4cp0
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