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Multi-Mapping Image-to-Image Translation with Central Biasing Normalization [article]

Xiaoming Yu, Zhenqiang Ying, Thomas Li, Shan Liu, Ge Li
2020 arXiv   pre-print
Based on the criteria, we propose central biasing normalization to inject the latent code information.  ...  To solve these problems, we propose the consistency within diversity criteria for designing the multi-mapping model.  ...  Then we apply the proposed central biasing normalization (CBN) to construct our central biasing generator (CBG) for multi-mapping translation. A.  ... 
arXiv:1806.10050v5 fatcat:hh3oxom3ibbidiys6brasrcpgm

Multi-mapping Image-to-Image Translation via Learning Disentanglement [article]

Xiaoming Yu, Yuanqi Chen, Thomas Li, Shan Liu, Ge Li
2019 arXiv   pre-print
Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation.  ...  Then, we encourage the generator to learn multi-mappings by a random cross-domain translation.  ...  Related Work Image-to-image translation. The problem of I2I is first defined by Isola et al. [16] .  ... 
arXiv:1909.07877v2 fatcat:rjoqvmfmvrewdjft2nvism7ugu

Multi-CartoonGAN with Conditional Adaptive Instance-Layer Normalization for Conditional Artistic Face Translation

Rina Komatsu, Tad Gonsalves
2022 AI  
Additionally, we report on the development of the conditional adaptive layer-instance normalization (CAdaLIN) process for use with our model to make it robust to unique feature translations.  ...  normalization to the process.  ...  [25] pointed out that injection methods can lead to mode collapses when used with existing normalization methods, such as batch or instance normalization, and proposed an alternative central biasing  ... 
doi:10.3390/ai3010003 fatcat:un24755nbnhqvit7c2la4geetq

SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning [article]

Xiaoming Yu, Xing Cai, Zhenqiang Ying, Thomas Li, Ge Li
2020 arXiv   pre-print
In this paper, we propose a novel method, SingleGAN, to perform multi-domain image-to-image translations with a single generator.  ...  Image translation is a burgeoning field in computer vision where the goal is to learn the mapping between an input image and an output image.  ...  So we adopt the central biasing instance normalization (CBIN) proposed in [22] to inject the domain code in our SingleGAN model.  ... 
arXiv:1810.04991v2 fatcat:34ln4uqkpjan5fmjdnjbvcnt2e

MR-contrast-aware image-to-image translations with generative adversarial networks

Jonas Denck, Jens Guehring, Andreas Maier, Eva Rothgang
2021 International Journal of Computer Assisted Radiology and Surgery  
Results This enables us to synthesize MR images with adjustable image contrast.  ...  It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.  ...  The AdaIN operation is defined as: Each feature map x is normalized separately with its mean (x) and standard deviation (x) , then scaled and biased through learned transformations , , given the label  ... 
doi:10.1007/s11548-021-02433-x pmid:34148167 pmcid:PMC8616894 fatcat:vxb6lqgknng6jkdywnba6zftnm

Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation [article]

Simyung Chang, SeongUk Park, John Yang, Nojun Kwak
2019 arXiv   pre-print
Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network.  ...  We propose a method that expands the concept of 'multi-domain' from data to the loss area and learns the combined characteristics of each domain to dynamically infer translations of images in mixed domains  ...  central biasing normalization [26] .  ... 
arXiv:1811.12362v3 fatcat:kuhao5owsra5zerhfohebxuawq

QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis

Marc Pereyra, Armin Drusko, Franziska Krämer, Frederic Strobl, Ernst H. K. Stelzer, Franziska Matthäus
2021 BMC Bioinformatics  
We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.  ...  Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass.  ...  These biases are completely avoided by using NSQECC, which detects the underlying translation with 100% accuracy given a sufficiently large search margin (see Figure S1 ).  ... 
doi:10.1186/s12859-021-04474-0 pmid:34863116 pmcid:PMC8642913 fatcat:j4zrb6n7ifejjani7ssz47vaoe

Input-level Inductive Biases for 3D Reconstruction [article]

Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, Andrew Zisserman
2022 arXiv   pre-print
In this paper we tackle 3D reconstruction using a domain agnostic architecture and study how instead to inject the same type of inductive biases directly as extra inputs to the model.  ...  In particular we study how to encode cameras, projective ray incidence and epipolar geometry as model inputs, and demonstrate competitive multi-view depth estimation performance on multiple benchmarks.  ...  We thank Yi Yang for providing advice regarding data processing and Jean-Baptiste Alayrac for his help with the training pipeline.  ... 
arXiv:2112.03243v2 fatcat:m7fa6i5iwzbh7i22hnwedgmjla

Synchronization and Self-Calibration for Helmet-Held Consumer Cameras, Applications to Immersive 3D Modeling and 360 Video

Maxime Lhuillier, Thanh-Tin Nguyen
2015 2015 International Conference on 3D Vision  
We experiment both synchronization and self-calibration on four GoPro cameras mounted on a helmet, such that the resulting multi-camera is assumed to be central and provides a 360 degree field-of-view  ...  Our assumptions are easy to meet in practice: the cameras have the same setting (frequency, image resolution, field-of-view, roughly equiangular).  ...  The registration is defined by rotation R, which maps one ray set to another (no translation since the calibrations are central in our paper).  ... 
doi:10.1109/3dv.2015.56 dblp:conf/3dim/LhuillierN15 fatcat:bo6pf7vuxbh5ngfzqhfbwgjmdi

Registration of Images With Outliers Using Joint Saliency Map

Binjie Qin, Zhijun Gu, Xianjun Sun, Yisong Lv
2010 IEEE Signal Processing Letters  
However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and biased maxima.  ...  We propose a novel joint saliency map (JSM) to highlight the corresponding salient structures in the two images, and emphatically group those salient structures into the smoothed compact clusters in the  ...  Wang for her help to our algorithm.  ... 
doi:10.1109/lsp.2009.2033728 fatcat:ab6t2ifc7jb77kvbbyzrl2gbhi

MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks [article]

Jonas Denck, Jens Guehring, Andreas Maier, Eva Rothgang
2021 arXiv   pre-print
Results This enables us to synthesize MR images with adjustable image contrast.  ...  As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required.  ...  The AdaIN operation is defined as: ( , ) = ( ) • ( − ( ) ( ) ) + ( ) (4) Each feature map is normalized separately with its mean ( ) and standard deviation ( ), then scaled and biased through learned affine  ... 
arXiv:2104.01449v1 fatcat:qxlmnp2nrvatrjm7jljzg6odee

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona [article]

Valentina Salvatelli, Souvik Bose, Brad Neuberg, Luiz F. G. dos Santos, Mark Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin
2019 arXiv   pre-print
translation.  ...  Towards this end we developed a deep neural network, structured as an encoder-decoder with skip connections (U-Net), that reconstructs the Sun's image of one instrument channel given temporally aligned  ...  The authors wish to thank in particular IBM and Google Cloud for generously providing computing resources.  ... 
arXiv:1911.04006v1 fatcat:bpuzba4hnnhi5dvcsucl4rahtu

A cortical framework for invariant object categorization and recognition

João Rodrigues, J. M. Hans du Buf
2009 Cognitive Processing  
The model is a functional but dichotomous one, because keypoints are employed to model the "where" data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation  ...  maps for Focus-of-Attention.  ...  We present a new model for obtaining 2D translation, rotation and size invariance by dynamic mapping of saliency maps based on multi-scale keypoint information.  ... 
doi:10.1007/s10339-009-0262-2 pmid:19471984 fatcat:flsv3zuv3fcsba6h6halhssjra

DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations [article]

Srinivas S. S. Kruthiventi, Kumar Ayush, R. Venkatesh Babu
2015 arXiv   pre-print
DeepFix is designed to capture semantics at multiple scales while taking global context into account using network layers with very large receptive fields.  ...  Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end  ...  In contrast to the traditional usage of multi-scale hand crafted image features, Kümmerer et al.  ... 
arXiv:1510.02927v1 fatcat:qcjmd3o23bcbpph4f2wdb7jlwq

Radiometric Calibration of RapidScat Using the GPM Microwave Imager

Ali Al-Sabbagh, Ruaa Alsabah, Josko Zec
2018 Proceedings (MDPI)  
Normalization was based on the radiative transfer model (RTM) to yield an equivalent brightness temperature prior to the direct comparison with RapidScat.  ...  This work also presents the radiometric (passive mode) cross-calibration using the GPM (Global Precipitation Measurement) Microwave Imager (GMI) as a reference to eliminate the measurement biases of brightness  ...  The authors also wish to acknowledge the collaboration with W. Linwood Jones at Central Florida Remote Sensing Laboratory, University of Central Florida.  ... 
doi:10.3390/ecrs-2-05137 fatcat:nvwinoedcfeejkggkzgpcab7li
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