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Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability
Technological advances in the automotive industry are bringing automated...
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Psychophysiological responses to takeover requests in conditionally automated driving
In SAE Level 3 automated driving, taking over control from automation ra...
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Emotional Design
Emotional design has been well recognized in the domain of human factors...
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Physical-Layer Security for Two-Hop Air-to-Underwater Communication Systems With Fixed-Gain Amplify-and-Forward Relaying
We analyze a secure two-hop mixed radio frequency (RF) and underwater wi...
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Visible Feature Guidance for Crowd Pedestrian Detection
Heavy occlusion and dense gathering in crowd scene make pedestrian detec...
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Matching Guided Distillation
Feature distillation is an effective way to improve the performance for ...
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Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression
Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high ...
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Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks
Hawkes process provides an effective statistical framework for analyzing...
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Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
We present the Additive Poisson Process (APP), a novel framework that ca...
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Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
Face anti-spoofing is critical to the security of face recognition syste...
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Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Face anti-spoofing (FAS) plays a vital role in face recognition systems....
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Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving
In conditionally automated driving, drivers have difficulty in takeover ...
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Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation
In this paper, we consider the sigmoid Gaussian Hawkes process model: th...
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Recognizing Part Attributes with Insufficient Data
Recognizing attributes of objects and their parts is important to many c...
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Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants
The stationarity is assumed in vanilla Hawkes process, which reduces the...
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Efficient EM-Variational Inference for Hawkes Process
In classical Hawkes process, the baseline intensity and triggering kerne...
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Compact Generalized Non-local Network
The non-local module is designed for capturing long-range spatio-tempora...
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Fine-grained Video Categorization with Redundancy Reduction Attention
For fine-grained categorization tasks, videos could serve as a better so...
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Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories
Existing 3D pose datasets of object categories are limited to generic ob...
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Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
Attention-based learning for fine-grained image recognition remains a ch...
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3D Pose Estimation for Fine-Grained Object Categories
Existing object pose estimation datasets are related to generic object t...
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Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Automated facial expression analysis has a variety of applications in hu...
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Deep Metric Learning with Angular Loss
The modern image search system requires semantic understanding of image,...
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Dynamic Computational Time for Visual Attention
We propose a dynamic computational time model to accelerate the average ...
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Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
This paper proposes a novel deep learning framework named bidirectional-...
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Fully Convolutional Attention Networks for Fine-Grained Recognition
Fine-grained recognition is challenging due to its subtle local inter-cl...
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Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Existing fine-grained visual categorization methods often suffer from th...
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Embedding Label Structures for Fine-Grained Feature Representation
Recent algorithms in convolutional neural networks (CNN) considerably ad...
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Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Given a food image, can a fine-grained object recognition engine tell "w...
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