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Found 8 papers, 4 papers with code
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression
In the temporal context, the complex time-varying system behaviors can be revealed by the temporal modes in the proposed model.
Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting
Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary.
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network
For hourly PM2. 5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2. 5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy.
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships.
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions.
PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification
In this paper, we introduce a novel unsupervised pose augmentation cross-view person Re-Id scheme called PAC-GAN to overcome these limitations.
Cross-Modal Person Re-Identification
Generative Adversarial Network
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DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition
Using the quantization method proposed, we were able to achieve performances closer to that of full-precision counterpart.
Crossing Generative Adversarial Networks for Cross-View Person Re-identification
Given a pair of person images, the proposed model consists of the variational auto-encoder to encode the pair into respective latent variables, a proposed cross-view alignment to reduce the view disparity, and an adversarial layer to seek the joint distribution of latent representations.
Cross-Modal Person Re-Identification
Generative Adversarial Network
