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Unsupervised Video Interpolation Using Cycle Consistency
[article]
2021
arXiv
pre-print
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Here, we propose unsupervised techniques to synthesize high frame rate videos directly from low frame rate videos using cycle consistency. ...
The pseudo supervised loss term, used together with cycle consistency, can effectively adapt a pre-trained model to a new target domain. ...
To the best of our knowledge, this is the first attempt to use a cycle consistency constraint to learn video interpolation in a completely unsupervised way. ...
arXiv:1906.05928v3
fatcat:pot3jbsnang4hkn3ctmezj2wey
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning
[article]
2020
arXiv
pre-print
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Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent. ...
The style-content decomposition mechanism enables us to achieve style consistent video translation results as well as provides us with a good interface for modality flexible translation. ...
Video interpolation loss weight λ interp is set to 10. Cycle consistency loss weight λ cycle is set to 10. Style reconstruction loss weight λ rec is set to 0.025. ...
arXiv:2004.06502v1
fatcat:237a7g7plrg2piehgri6rzfju4
Joint Wasserstein Distribution Matching
[article]
2020
arXiv
pre-print
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In the experiments, we apply our method to unsupervised image translation and cross-domain video synthesis. ...
To be specific, DVF produces videos by video interpolation in one domain, and CycleGAN translates the videos from one domain to another domain. • DVM-Cycle uses a geometrical view synthesis DVM [14] ...
for video synthesis, and then uses CycleGAN to translate the generated video to another domain. • AdaConv-Cycle combines a state-of-the-art video interpolation method AdaConv [25] with a pre-trained ...
arXiv:2003.00389v1
fatcat:qcbcpvwforhbpp55xye2vk2u4m
Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video
[article]
2020
arXiv
pre-print
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Performing low hertz labeling for surgical videos at intervals can greatly releases the burden of surgeons. ...
By exploiting generated data pairs, our framework can recover and even enhance temporal consistency of training sequences to benefit segmentation. ...
Unsupervised Cycle Loss. The key idea is to learn the motion flow that can encourage models to satisfy cycle consistency in time domain. ...
arXiv:2007.02501v1
fatcat:c53hdwpcmfgyde4i7zl7qcmth4
Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation
[article]
2020
arXiv
pre-print
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We formulate this cycle-consistency using two mapping functions; frame extrapolation and interpolation. ...
Cycle-consistency loss has been used for stable prediction between two function spaces in many visual tasks. ...
In [16] , they proposes cycle-consistency loss between two mapping functions from scratch for unsupervised image translation. ...
arXiv:2005.13194v1
fatcat:tokb5ghchbbc7gmwuyrzpeo5yy
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
[article]
2021
arXiv
pre-print
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To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised ...
Moreover, a new cycle-consistency constraint is proposed to supervise this cyclic procedure, which encourages the network to reconstruct more realistic HR images. ...
To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet), in which a new two-stage SSL strategy is developed to train an interpolation network in an unsupervised manner ...
arXiv:2106.15395v1
fatcat:c4kuxocmkjgclfu7ldhqcvarty
Learning to Track Objects from Unlabeled Videos
[article]
2021
arXiv
pre-print
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Second, we train a naive Siamese tracker from scratch using single-frame pairs. ...
Extensive experiments show that the proposed USOT learned from unlabeled videos performs well over the state-of-the-art unsupervised trackers by large margins, and on par with recent supervised deep trackers ...
The contemporary method of UDT is TimeCycle [41] , which proposes cycle consistency to generate unsupervised video representation. ...
arXiv:2108.12711v1
fatcat:47sebbdsevgjnbbm3cizuy7n6e
Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow
[article]
2022
arXiv
pre-print
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Thereafter, we utilize our unsupervised recycle and unsupervised spatial loss, guided by the pseudo-supervision provided by the synthetic optical flow, to accurately enforce spatiotemporal consistency ...
Experiments show that our method is versatile in various scenarios and achieves state-of-the-art performance in generating temporally and semantically consistent videos. ...
UR represents unsupervised recycle loss, US represents unsupervised spatial loss, VDRS represents video domain regularization suppression and Cyc represents cycle loss. through synthetic optical flow and ...
arXiv:2201.05723v1
fatcat:fnpwntcmmzfste4h6jhoommumq
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
[article]
2020
arXiv
pre-print
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motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based ...
Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation methods that have been extended to support volumetric ...
cycles in functional organ structures. ...
arXiv:2002.12680v2
fatcat:zd2scrkoq5h6lbyflhgrer6yby
Direct Video Frame Interpolation with Multiple Latent Encoders
2021
IEEE Access
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INDEX TERMS Video interpolation, 360 • video interpolation, latent space learning, convolutional neural network. ...
We present a simple but effective video interpolation framework that can be applied to various types of videos including conventional videos and 360 • videos. ...
[23] propose a cycle consistency loss to train an optical flow estimation network. ...
doi:10.1109/access.2021.3053695
fatcat:dhjzlzrcozg6zjms74h6her7ze
Interp-SUM: Unsupervised Video Summarization with Piecewise Linear Interpolation
2021
Sensors
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In this paper, we propose an unsupervised video summarization method with piecewise linear interpolation (Interp-SUM). ...
This paper addresses the problem of unsupervised video summarization. Video summarization helps people browse large-scale videos easily with a summary from the selected frames of the video. ...
However, the model adopts a cycle generative adversarial network for preserving the information of the original video in the summary video. In [16] , they proposed an unsupervised SUM-FCN. ...
doi:10.3390/s21134562
fatcat:jjn655pcybbj5ove65lphehzae
Gene Prediction
[chapter]
2012
Msphere
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• Prodigal was built using a trial and error approach • A set of curated genomes that had been analyzed using the JGI ORNL pipeline (http://genome.ornl.gov/) was used • This pipeline consisted of a combination ...
Interpolated Markov Models and Gene Prediction Interpolating amongst different pattern lengths allows us to use the maximum pattern length with minimal impact on parameter resolution. ...
doi:10.1007/978-1-61779-582-4_6
pmid:22407709
fatcat:v7n67td27zagreqczqim27b7te
Recycle-GAN: Unsupervised Video Retargeting
[article]
2018
arXiv
pre-print
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We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's ...
In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. ...
Cycle loss: Zhu et al. [53] use cycle consistency [51] to define a reconstruction loss when the pairs are not available. ...
arXiv:1808.05174v1
fatcat:2vychysagvf43molzagxxtvxqi
Recycle-GAN: Unsupervised Video Retargeting
[chapter]
2018
Lecture Notes in Computer Science
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We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's ...
In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. ...
Cycle loss: Zhu et al. [53] use cycle consistency [51] to define a reconstruction loss when the pairs are not available. ...
doi:10.1007/978-3-030-01228-1_8
fatcat:y35ontbezngepnysioqzs6leiu
Neural style-preserving visual dubbing
2019
ACM Transactions on Graphics
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1909.02518v1 fatcat:nih37tqmdveercoooxpfse3iseOther Versions
We train our model with unsynchronized source and target videos in an unsupervised manner using cycle-consistency and mouth expression losses, and synthesize photorealistic video frames using a layered ...
Dubbing is a technique for translating video content from one language to another. ...
We train this network in an unsupervised manner on unpaired videos using cycle-consistency and mouth expression losses. ...
doi:10.1145/3355089.3356500
fatcat:dmtkieupszhvbmab466hxlmjyu
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