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Found 10 papers, 2 papers with code
TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection
Arbitrary-shaped text detection is a challenging task due to the complex geometric layouts of texts such as large aspect ratios, various scales, random rotations and curve shapes.
S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving
To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles require to sufficiently analyze the motion patterns of surrounding traffic-agents and accurately predict their future trajectories.
Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency Detection
As a result, a key issue in video saliency detection is how to effectively capture the intrinsical properties of atomic video structures as well as their associated contextual interactions along the spatial and temporal dimensions.
Geometry-Aware Scene Text Detection With Instance Transformation Network
Localizing text in the wild is challenging in the situations of complicated geometric layout of the targets like random orientation and large aspect ratio.
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps
We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem.
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure.
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation
Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently.
Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks
The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption.
Semiparametric Regression for Spatial Data via Deep Learning
To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence.
