1,499 Result(s)
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Chapter and Conference Paper
Decoupled Contrastive Learning for Long-Tailed Distribution
Self-supervised contrastive learning is popularly used to obtain powerful representation models. However, unlabeled data in the real world naturally exhibits a long-tailed distribution, making the traditional ...
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Chapter and Conference Paper
DeepChrom: A Diffusion-Based Framework for Long-Tailed Chromatin State Prediction
Chromatin state reflects distinct biological roles of the genome that can systematically characterize regulatory elements and their functional interaction. Despite extensive computational studies, accurate pre...
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Chapter and Conference Paper
Robust Object Recognition and Command Understanding for a House Tidying-Up Robot
In this study, a robust object recognition and command understanding system for a house tidying-up robot is proposed. The robot can understand the user’s intentions by using a speech recognition system. When t...
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Chapter and Conference Paper
Blendshape-Based Migratable Speech-Driven 3D Facial Animation with Overlap** Chunking-Transformer
Speech-driven 3D facial animation has attracted an amount of research and has been widely used in games and virtual reality. Most of the latest state-of-the-art methods employ Transformer-based architecture wi...
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Chapter and Conference Paper
Autoencoder and Masked Image Encoding-Based Attentional Pose Network
Despite recent advances in single-image-based 3D human pose and shape estimation, partial occlusion remains a major challenge for many methods, leading to significant prediction errors. Some existing methods ...
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Chapter and Conference Paper
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
The task of radiology reporting comprises describing and interpreting the medical findings in radiographic images, including description of their location and appearance. Automated approaches to radiology repo...
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Chapter and Conference Paper
Advancing Short-Term Traffic Congestion Prediction: Navigating Challenges in Learning-Based Approaches
Traffic congestion prediction has already become a significant aspect of modern transportation systems. By predicting traffic congestion, transportation planners and traffic management agencies can take steps ...
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Chapter and Conference Paper
Graph Structure Learning-Based Compression Method for Convolutional Neural Networks
Convolutional neural networks (CNNs) have achieved remarkable performance in diverse applications. Nevertheless, the substantial scale and computational intricacy limit the practical implementation of CNNs, pa...
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Chapter and Conference Paper
Ensemble of Randomized Neural Network and Boosted Trees for Eye-Tracking-Based Driver Situation Awareness Recognition and Interpretation
Ensuring traffic safety is crucial in the pursuit of sustainable transportation. Across diverse traffic systems, maintaining good situation awareness (SA) is important in promoting and upholding traffic safety...
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Chapter and Conference Paper
Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI
Recently, resting-state functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has greatly boosted diagnostic performance of brain diseases in a manner that can refine FCN embed...
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Chapter and Conference Paper
Neuron Pruning-Based Federated Learning for Communication-Efficient Distributed Training
Efficient and flexible cloud computing is widely used in distributed systems. However, in the Internet of Things (IoT) environment with heterogeneous capabilities, the performance of cloud computing may declin...
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Chapter and Conference Paper
LearnedSync: A Learning-Based Sync Optimization for Cloud Storage
Cloud sync refers to the synchronization (sync) between devices for files that live on cloud storage. Its efficiency is critical to delivering on the promise of anywhere and anytime access for individuals, gro...
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Chapter and Conference Paper
A Robust Detection and Correction Framework for GNN-Based Vertical Federated Learning
Graph Neural Network based Vertical Federated Learning (GVFL) facilitates data collaboration while preserving data privacy by learning GNN-based node representations from participants holding different dimensi...
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Chapter and Conference Paper
Moving-Target Enclosing Control for Multiple Nonholonomic Mobile Agents Under Input Disturbances
This paper investigates a moving-target enclosing control problem of multiple nonholonomic mobile agents subject to unknown heterogeneous input disturbances. The agents are required to move around the target a...
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Chapter and Conference Paper
Multi-level Temporal Information Sharing Transformer-Based Feature Reuse Network for Cardiac MRI Reconstruction
The accurate reconstruction of accelerated Magnetic Resonance Imaging (MRI) brings significant clinical benefits, including improved diagnostic accuracy and reduced examination costs. Traditional cardiac MRI r...
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Chapter and Conference Paper
FaCa: Fast Aware and Competition-Avoided Balancing for Data Center Network
Nowadays, the scale of business data is expanding at an unprecedented rate. To cater to the needs of large businesses, data center networks (DCNs) have been widely deployed and are continuing to expand. Howeve...
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Chapter and Conference Paper
Research on Fuzzy Weighted Controller for Battery Discharge of Dual-Channel Dual-Active Bridge
Aiming at the dual closed-loop control of dual-active bridge (DAB) charging and discharging circuits in energy storage devices, which is difficult to allocate discharging current reasonably based on battery pe...
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Chapter and Conference Paper
AGST-LSTM: The ConvLSTM Model Combines Attention and Gate Structure for Spatiotemporal Sequence Prediction Learning
Spatiotemporal sequence prediction learning generates one or more frames of images by learning from multiple frames of historical input. Most current spatiotemporal sequence prediction learning methods do not ...
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Chapter and Conference Paper
DFAR-Net: Dual-Input Three-Branch Attention Fusion Reconstruction Network for Polarized Non-Line-of-Sight Imaging
Polarized non-line-of-sight (NLOS) imaging is a promising visual perception technique for enhancing the visibility of occluded objects hidden behind walls. The main challenge of this task is that conventional ...
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Chapter and Conference Paper
Camouflaged Object Detection via Global-Edge Context and Mixed-Scale Refinement
Camouflage object detection (COD), trying to segment objects that blend perfectly with the surrounding environment, is challenging and complex in real-world scenarios. However, existing deep learning methods o...
