129 Result(s)
within
English
Computer Imaging, Vision, Pattern Recognition and Graphics
Machine Learning
**ze Shen
Chapter
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Chapter and Conference Paper
MoViT: Memorizing Vision Transformers for Medical Image Analysis
The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for var...
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Chapter and Conference Paper
Feature Matching in the Changed Environments for Visual Localization
Robust feature matching is a fundamental capability for visual SLAM. It remains, however, a challenging task, particularly for changed environments. Some researchers use semantic segmentation to remove potenti...
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Chapter and Conference Paper
MaskDiffuse: Text-Guided Face Mask Removal Based on Diffusion Models
As masked face images can significantly degrade the performance of face-related tasks, face mask removal remains an important and challenging task. In this paper, we propose a novel learning framework, called ...
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Chapter and Conference Paper
Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion
Magnetic resonance (MR) images collected in 2D scanning protocols typically have large inter-slice spacing, resulting in high in-plane resolution but reduced through-plane resolution. Super-resolution techniqu...
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Chapter and Conference Paper
A Review of Image and Point Cloud Fusion in Autonomous Driving
In the task of autonomous driving perception scenarios, multi-sensor fusion is gradually becoming the current mainstream trend. At this stage, researchers use multimodal fusion to leverage information and ulti...
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Chapter and Conference Paper
Tailoring Large Language Models to Radiology: A Preliminary Approach to LLM Adaptation for a Highly Specialized Domain
In this preliminary work, we present a domain fine-tuned LLM model for radiology, an experimental large language model adapted for radiology. This model, created through an exploratory application of instructi...
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Chapter and Conference Paper
AU-Oriented Expression Decomposition Learning for Facial Expression Recognition
Facial Expression Recognition (FER) has received extensive attention in recent years. Due to the strong similarity between expressions, it is urgent to distinguish them meticulously in a finer-grained manner. ...
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Chapter and Conference Paper
Identifying Alzheimer’s Disease-Induced Topology Alterations in Structural Networks Using Convolutional Neural Networks
Identifying topology alterations in white matter connectivity has emerged as a promising avenue for exploring potential markers of Alzheimer’s disease (AD). However, conventional graph learning methods struggl...
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Chapter and Conference Paper
Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation
Existing thyroid nodule segmentation methods are primarily developed based on ultrasound images, which generally neglects the clinical reports that include rich semantic information for nodules. However, curre...
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Chapter and Conference Paper
Learning Adapters for Text-Guided Portrait Stylization with Pretrained Diffusion Models
This paper presents a framework for text-guided face portrait stylization using a pre-trained large-scale diffusion model. To balance style transformation and content preservation, we introduce an adapter that...
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Chapter and Conference Paper
Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance
The infant brain develops dramatically during the first two years of life. Accurate segmentation of brain tissues is essential to understand the early development of both normal and disease changes. However, t...
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Chapter and Conference Paper
Regionalized Infant Brain Cortical Development Based on Multi-view, High-Level fMRI Fingerprint
The human brain demonstrates higher spatial and functional heterogeneity during the first two postnatal years than any other period of life. Infant cortical developmental regionalization is fundamental for ill...
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Chapter and Conference Paper
Cross-view Contrastive Mutual Learning Across Masked Autoencoders for Mammography Diagnosis
Mammography is a widely used screening tool for breast cancer, and accurate diagnosis is critical for the effective management of breast cancer. In this study, we propose a novel cross-view mutual learning met...
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Chapter and Conference Paper
Deepfake Detection Performance Evaluation and Enhancement Through Parameter Optimization
Deepfake technology has become a subject of concern due to its potential for spreading misinformation and facilitating deceptive activities. To address these issues, various deepfake detection approaches have ...
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Chapter and Conference Paper
Research on Virtual Data Set Generation for Ship Target Recognition at Sea
Deep learning provides a feasible and effective method for ship target recognition and state estimation. However, it takes a lot of manpower and financial resources to collect and label images from real sea sc...
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Chapter and Conference Paper
Design of Accelerators for Combined Infrared and Visible Image Target Detection Based on Deep Learning
The combined infrared and visible target detection based on deep learning attracts much attention because of the satisfactory performance. However, the improved capability of multi-modal detection results in a...
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Chapter and Conference Paper
Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network
Fetal brain magnetic resonance imaging (MRI) is becoming more important for early brain assessment in prenatal examination. Fast acquisition of three cross-sectional series/views is often used to eliminate mot...
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Chapter and Conference Paper
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While most prevalent methods progressively downscale the 3D point clouds and camera images...
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Chapter and Conference Paper
Contrastive Learning-Based Breast Tumor Segmentation in DCE-MRI
Precise and automated segmentation of tumors from breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI) is crucial for obtaining quantitative morphological and functional information, thereby as...
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Chapter and Conference Paper
FAST-Net: A Coarse-to-fine Pyramid Network for Face-Skull Transformation
Face-skull transformation, i.e., shape transformation between facial surface and skull structure, has a wide range of applications in various fields such as forensic facial reconstruction and craniomaxillofaci...
