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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
for Metal Artifact Reduction in CT Images of the Head 673 Deep Recurrent Level Set for Segmenting Brain Tumors 676 Deep convolutional filtering for spatio-temporal denoising and artifact removal in arterial  ...  Detection of Fetal Standardized Planes Assisted By Generated Sonographer Attention Maps 500 Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling 503 Spatio-Temporal  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Prediction [article]

Divya Saxena, Jiannong Cao
2021 arXiv   pre-print
Spatio-temporal (ST) data for urban applications, such as taxi demand, traffic flow, regional rainfall is inherently stochastic and unpredictable.  ...  To handle the aforementioned issues, in this paper, we propose a novel deep generative adversarial network based model (named, D-GAN) for more accurate ST prediction by implicitly learning ST feature representations  ...  INTRODUCTION In recent times, developing efficient urban applications using spatio-temporal (ST) data, such as air and water quality forecasting [1] , crowd flows prediction [2] , cellular traffic prediction  ... 
arXiv:1907.08556v3 fatcat:hprxaoi2ibbpje7g3tg6x2wqke

Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning [article]

Kangning Liu, Shuhang Gu, Andres Romero, Radu Timofte
2020 arXiv   pre-print
Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way.  ...  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.  ...  They trained a temporal predictor to predict the next frame based on two past frames, and plugged the temporal predictor in the cycle-loss to impose the spatio-temporal constraint on the traditional image-level  ... 
arXiv:2004.06502v1 fatcat:237a7g7plrg2piehgri6rzfju4

Probabilistic Future Prediction for Video Scene Understanding [article]

Anthony Hu, Fergal Cotter, Nikhil Mohan, Corina Gurau, Alex Kendall
2020 arXiv   pre-print
Our model learns a representation from RGB video with a spatio-temporal convolutional module.  ...  We present a novel deep learning architecture for probabilistic future prediction from video.  ...  [39] propose generating multi-modal futures with adversarial training, however spatio-temporal discriminator networks are known to suffer from mode collapse [23].  ... 
arXiv:2003.06409v2 fatcat:mf56dimeh5hgjpijm2yyeibhzu

Flow-based Spatio-Temporal Structured Prediction of Dynamics [article]

Mohsen Zand, Ali Etemad, Michael Greenspan
2022 arXiv   pre-print
It combines deterministic and stochastic representations with CNFs to create a probabilistic neural generative approach that can model the variability seen in high-dimensional structured spatio-temporal  ...  Their effectiveness in modelling multivariates spatio-temporal structured data has yet to be completely investigated.  ...  We compare our method with a baseline FCN (fully convolutional network), and structured prediction algorithms of DVN (deep value networks) [73] , c-Glow (conditional Glow) [25] , and ALEN (adversarial  ... 
arXiv:2104.04391v2 fatcat:adddsj6dfzbldk2p2zgkzuq6li

Speech Prediction in Silent Videos using Variational Autoencoders [article]

Ravindra Yadav, Ashish Sardana, Vinay P Namboodiri, Rajesh M Hegde
2020 arXiv   pre-print
In this paper, we present a stochastic model for generating speech in a silent video.  ...  It can lead to low-quality predictions as the model collapses to optimizing the average behavior rather than learning the full data distributions.  ...  However, due to the high dimensionality of audio and video streams and spatio-temporal complexities in natural videos, predicting the audio modality from raw sensory observations such as videos is exceptionally  ... 
arXiv:2011.07340v1 fatcat:2fbzr4krlrbblaozsyco37e4g4

A Review on Deep Learning Techniques for Video Prediction [article]

Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, Antonis Argyros
2020 arXiv   pre-print
The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis.  ...  The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems.  ...  Featuring a novel spatio-temporal module, their five-component architecture learns rich representations that incorporate both local and global spatio-temporal context.  ... 
arXiv:2004.05214v2 fatcat:weerbkanmjb4dn6wkn5o4b5aia

DanceConv: Dance Motion Generation with Convolutional Networks

Kosmas Kritsis, Aggelos Gkiokas, Aggelos Pikrakis, Vassilis Katsouros
2022 IEEE Access  
Recent approaches have mostly used recurrent neural networks (RNNs), which are known to suffer from prediction error accumulation, usually limiting models to synthesize short choreographies of less than  ...  Based on this outcome, we train the proposed multimodal architecture with two different approaches, namely teacher-forcing and self-supervised curriculum learning, to deal with the autoregressive error  ...  spatio-temporal complexity that reflects the non-linear relationship with the accompanying musical content [13] .  ... 
doi:10.1109/access.2022.3169782 fatcat:abjqqrrww5bulh5tadggkwzk4u

Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks [article]

Benedikt Mersch, Thomas Höllen, Kun Zhao, Cyrill Stachniss, Ribana Roscher
2021 arXiv   pre-print
Using our neighborhood-based data representation, the proposed system jointly exploits correlations in the spatial and temporal domain using convolutional neural networks.  ...  Interdependencies between vehicle behaviors and the multimodal nature of future intentions in a dynamic and complex driving environment render trajectory prediction a challenging problem.  ...  To classify the future maneuver of the vehicle with our spatio-temporal CNN defined in Sec.  ... 
arXiv:2109.07365v1 fatcat:y7dzz4uu6jfz5fmzhuclee66zu

Trajectory prediction of cyclist based on spatial‐temporal multi‐graph network in crowded scenarios

Meng Li, Tao Chen, Hao Du
2021 Electronics Letters  
It reduces 9% prediction error when compared to recurrent neural network based models and is more effective in crowded scenarios.  ...  Instead of the recurrent architecture, a temporal convolution to forecast the future paths is introduced.  ...  Neural Network for pedestrian trajectory forecasting, (7) AST-GNN [13] , an attention-based spatio-temporal graph neural network for pedestrian trajectory prediction, (8) Graph-TCN [14] , a spatio-temporal  ... 
doi:10.1049/ell2.12374 fatcat:ojdjiifipvhjzapdp3wgvpovbu

Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model [article]

Alessandro Sebastianelli, Artur Nowakowski, Erika Puglisi, Maria Pia Del Rosso, Jamila Mifdal, Fiora Pirri, Pierre Philippe Mathieu, Silvia Liberata Ullo
2022 arXiv   pre-print
Related techniques have been analyzed for years with a progressively clearer view of the appropriate methods to adopt, from multi-spectral to inpainting methods.  ...  Quantitative and qualitative results prove that the proposed method obtains cloud-free images, preserving scene details without resorting to a huge portion of a clean image and coping with landscape changes  ...  The proposed model is a Spatio-Temporal Generator Network (STGAN) able to remove clouds by concatenating three previous image acquisitions.  ... 
arXiv:2106.12226v3 fatcat:osmwqwklpvfgdbkzxpsbafoh6e

IEEE Access Special Section Editorial: Artificial Intelligence (AI)-Empowered Intelligent Transportation Systems

Edith Ngai, Chao Chen, Amr M. Tolba, Mohammad S. Obaidat, Fanzhao Wang
2021 IEEE Access  
How can AI be integrated with ITS and function well in dynamic vehicular network scenarios?  ...  the rapid development of ubiquitous networks and smart vehicles.  ...  He is currently a Full Professor with the College of Computer Science, Chongqing University, Chongqing, China. He has published over 100 articles, including 20 in IEEE TRANSACTIONS.  ... 
doi:10.1109/access.2021.3074996 fatcat:dfyrghfswff6vmdlpa55jxtkjm

Future Frame Prediction of a Video Sequence [article]

Jasmeen Kaur, Sukhendu Das
2020 arXiv   pre-print
Recently, two distinct approaches have attempted to address this problem as: (a) use of latent variable models that represent underlying stochasticity and (b) adversarially trained models that aim to produce  ...  A latent variable model often struggles to produce realistic results, while an adversarially trained model underutilizes latent variables and thus fails to produce diverse predictions.  ...  RE-CALL mechanism helps to recall temporally distant memory. • Replace GAN with MG-GAN [1] : Generative adversarial networks are susceptible to mode collapse.  ... 
arXiv:2009.01689v1 fatcat:d7d3nhqicvggfpydhnuoouocni

Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs [article]

Javad Amirian, Jean-Bernard Hayet, Julien Pettre
2019 arXiv   pre-print
It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene.  ...  This paper proposes a novel approach for predicting the motion of pedestrians interacting with others.  ...  In [20] , groups of agents are modeled as spatio-temporal graphs where edges (temporal and spatial) are associated to RNNs.  ... 
arXiv:1904.09507v2 fatcat:yqntxp462ffenfdutcfet4lvoy

SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal

Patrick Ebel, Yajin Xu, Michael Schmitt, Xiao Xiang Zhu
2022 IEEE Transactions on Geoscience and Remote Sensing  
We propose two models highlighting the benefits and use cases of SEN12MS-CR-TS: First, a multimodal multitemporal 3-D convolution neural network that predicts a cloud-free image from a sequence of cloudy  ...  Both approaches are evaluated experimentally, with their respective models trained and tested on SEN12MS-CR-TS.  ...  Fourth, the baseline spatio-temporal generative adversarial network (STGAN) denotes the "Branched ResNet generator [infra-red (IR)]" architecture of [12] .  ... 
doi:10.1109/tgrs.2022.3146246 fatcat:wqf63lrhsfae7hgwbm6tvlhkwe
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