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Pattern Recognition
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Chapter and Conference Paper
Abstracts Embeddings Evaluation: A Case Study of Artificial Intelligence and Medical Imaging for the COVID-19 Infection
During the COVID-19 pandemic, a huge amount of literature was produced covering different aspects of infection. The use of artificial intelligence (AI) in medical imaging has been shown to improve screening, d...
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Chapter and Conference Paper
Inter-modal Fusion Network with Graph Structure Preserving for Fake News Detection
The continued ferment of fake news on the network threatens the stability and security of society, prompting researchers to focus on fake news detection. The development of social media has made it challenging...
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Chapter and Conference Paper
SDPSAT: Syntactic Dependency Parsing Structure-Guided Semi-Autoregressive Machine Translation
The advent of non-autoregressive machine translation (NAT) accelerates the decoding superior to autoregressive machine translation (AT) significantly, while bringing about a performance decrease. Semi-autoregr...
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Chapter and Conference Paper
Video-Based Emotion Estimation Using Deep Neural Networks: A Comparative Study
In this study we investigate the effectiveness of deep neural networks in predicting valence and arousal solely from visual information of video sequences. Several recent Convolutional Neural Network (CNN) and...
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Chapter and Conference Paper
STA-Net: Reconstruct Missing Temperature Data of Meteorological Stations Using a Spatiotemporal Attention Neural Network
Reconstructing the missing meteorological site temperature data is of great significance for analyzing climate change and predicting related natural disasters, but is a trickily and urgently solved problem. In...
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Video-Based Face Recognition
Video-based face recognition is an active research topic because of a wide range of applications including visual surveillance, access control, video content analysis, etc. Compared to still face recognition, ...
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Chapter and Conference Paper
High-Resolution Self-attention with Fair Loss for Point Cloud Segmentation
Applying deep learning techniques to analyze point cloud data has emerged as a prominent research direction. However, the insufficient spatial and feature information integration within point cloud and unbalan...
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Chapter and Conference Paper
Uformer++: Light Uformer for Image Restoration
Based on UNet, numerous outstanding image restoration models have been developed, and Uformer is no exception. The exceptional restoration performance of Uformer is not only attributable to its novel modules b...
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Chapter and Conference Paper
Contrastive Kernel Subspace Clustering
As a class of nonlinear subspace clustering methods, kernel subspace clustering has shown promising performance in many applications. This paper focuses on the kernel selection problem in the kernel subspace c...
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Chapter and Conference Paper
MIC: An Effective Defense Against Word-Level Textual Backdoor Attacks
Backdoor attacks, which manipulate model output, have garnered significant attention from researchers. However, some existing word-level backdoor attack methods in NLP models are difficult to defend effectivel...
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Chapter and Conference Paper
Impulsive Accelerated Reinforcement Learning for \(H_\infty \) Control
This paper revisits reinforcement learning for \(H_\infty \) H ∞ ...
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Chapter and Conference Paper
Detection of Anomalies and Explanation in Cybersecurity
Histogram-based anomaly detectors have gained significant attention and application in the field of intrusion detection because of their high efficiency in identifying anomalous patterns. However, they fail to...
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Chapter and Conference Paper
Research on Path Optimization of Deep Sea Mining Vehicles Based on Intelligent Algorithms
Taking the path planning problem of deep-sea mining vehicles in deep-sea environments as the research object, a path planning model for deep-sea mining vehicles was established based on its characteristics, an...
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Chapter and Conference Paper
On-Device Learning with Binary Neural Networks
Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded CPUs. In this paper, we propos...
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Chapter and Conference Paper
Improving Person Re-identification Through Low-Light Image Enhancement
Person re-identification (ReID) is a popular area of research in the field of computer vision. Despite the significant advancements achieved in recent years, most of the current methods rely on datasets contai...
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Chapter and Conference Paper
MVFAN: Multi-view Feature Assisted Network for 4D Radar Object Detection
4D radar is recognized for its resilience and cost-effectiveness under adverse weather conditions, thus playing a pivotal role in autonomous driving. While cameras and LiDAR are typically the primary sensors u...
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Chapter and Conference Paper
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising appro...
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Chapter and Conference Paper
Multi-level Patch Transformer for Style Transfer with Single Reference Image
Despite the recent success of image style transfer with Generative Adversarial Networks (GANs), this task remains challenging due to the requirements of large volumes of style image data. In this work, we pres...
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Chapter
Image Classification: A Computer Vision Task
Computer vision retrieves knowledge from pictures. Image classification is an important part of this. In this chapter, you will learn that image classification is an integral part of many problems from compute...
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Chapter and Conference Paper
Correlation Analysis Between Insomnia Severity and Depressive Symptoms of College Students Based on Pseudo-Siamese Network
To explore the correlation between emotional mood and sleep quality in a college student population, we propose a new method based on pseudo-siamese network, which can quickly diagnose the causes of depression...
