Journal ArticleDOI
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks
TLDR
SDOF-GAN as discussed by the authors leverages a GAN model for which the generator estimates symmetric optical flow fields while the discriminator differentiates the real ground-truth flow field from a fake one by assessing the flow warping error.Abstract:
There is a growing consensus in computer vision that symmetric optical flow estimation constitutes a better model than a generic asymmetric one for its independence of the selection of source/target image. Yet, convolutional neural networks (CNNs), that are considered the de facto standard vision model, deal with the asymmetric case only in most cutting-edge CNNs-based optical flow techniques. We bridge this gap by introducing a novel model named SDOF-GAN: symmetric dense optical flow with generative adversarial networks (GANs). SDOF-GAN realizes a consistency between the forward mapping (source-to-target) and the backward one (target-to-source) by ensuring that they are inverse of each other with an inverse network. In addition, SDOF-GAN leverages a GAN model for which the generator estimates symmetric optical flow fields while the discriminator differentiates the “real” ground-truth flow field from a “fake” estimation by assessing the flow warping error. Finally, SDOF-GAN is trained in a semi-supervised fashion to enable both the precious labeled data and large amounts of unlabeled data to be fully-exploited. We demonstrate significant performance benefits of SDOF-GAN on five publicly-available datasets in contrast to several representative state-of-the-art models for optical flow estimation.read more
Citations
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Journal ArticleDOI
Auto Labeling Methods Developed Through Semi-Weakly Supervised Learning in Prognostics and Health Management Applications for Rolling Ball Bearing
TL;DR: A semi-weakly supervised learning method that creates label functions using a small amount of data and, consequently, combines weak supervision and semi-supervision methods to expand the labeled dataset for training is proposed.
Proceedings ArticleDOI
AFOM: Advanced Flow of Motion Detection Algorithm for Dynamic Camera Videos
TL;DR: The proposed Advanced Flow Of Motion (AFOM) algorithm takes advantage of motion estimation between two consecutive frames and induces the estimated motion back to the frame to provide an improved detection on the dynamic camera videos.
References
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Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
Proceedings ArticleDOI
Image-to-Image Translation with Conditional Adversarial Networks
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Proceedings ArticleDOI
Are we ready for autonomous driving? The KITTI vision benchmark suite
TL;DR: The autonomous driving platform is used to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection, revealing that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world.