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Found 28 papers, 14 papers with code
Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis
Underwater imaging grapples with challenges from light-water interactions, leading to color distortions and reduced clarity.
Underwater Image Restoration via Polymorphic Large Kernel CNNs
This work provides new insights into the potential of CNN-based approaches for challenging image restoration tasks in underwater environments.
An AI-driven multimodal smart home platform for continuous monitoring and intelligent assistance in post-stroke patients
Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation.
Wearable intelligent throat enables natural speech in stroke patients with dysarthria
Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments.
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity.
High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer
To address this challenge, we construct StainDoc, the first large-scale, high-resolution ($2145\times2245$) dataset specifically designed for document stain removal.
IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities
Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes.
TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generation
The CPM module delves into the semantic dimensions of category-agnostic attributes, encapsulating them within a discrete codebook.
Test-Time Intensity Consistency Adaptation for Shadow Detection
Shadow detection is crucial for accurate scene understanding in computer vision, yet it is challenged by the diverse appearances of shadows caused by variations in illumination, object geometry, and scene context.
Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation
Overall, our results indicate that CMAformer, combined with the feature fusion framework and the new consistency loss, demonstrates strong complementarity in semi-supervised learning ensembles.
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation
To address this limitation, we propose the Adaptive Semantic Segmentation Network (ASSNet), a transformer architecture that effectively integrates local and global features for precise medical image segmentation.
SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation
First, a Synergistic Multi-Attention (SMA) Transformer block is proposed, which has the benefits of Pixel Attention, Channel Attention, and Spatial Attention for feature enrichment.
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life
In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+3
Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data
In this study, we introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children using only fundus images and baseline refraction data.
Dual-Hybrid Attention Network for Specular Highlight Removal
Specular highlight removal plays a pivotal role in multimedia applications, as it enhances the quality and interpretability of images and videos, ultimately improving the performance of downstream tasks such as content-based retrieval, object recognition, and scene understanding.
MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal
The low-frequency part typically contains illumination information, while the high-frequency part contains detailed content information.
Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition
Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing.
Adaptive Query Prompting for Multi-Domain Landmark Detection
Thanks to the lightweight nature of the decoder and AQP, we can handle multiple datasets by sharing the backbone encoder and then only perform partial parameter tuning without incurring much additional cost.
QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generation
The study of music-generated dance is a novel and challenging Image generation task.
MedPrompt: Cross-Modal Prompting for Multi-Task Medical Image Translation
Within this framework, we introduce the Prompt Extraction Block and the Prompt Fusion Block to efficiently encode the cross-modal prompt.
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI
In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner.
High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net
We handle high-resolution document shadow removal directly via a larger-scale real-world dataset and a carefully designed frequency-aware network.
Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer With Adaptive Channel Expansion
Vignetting commonly occurs as a degradation in images resulting from factors such as lens design, improper lens hood usage, and limitations in camera sensors.
DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal
Shadows in scanned documents pose significant challenges for document analysis and recognition tasks due to their negative impact on visual quality and readability.
Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline
Brain network analysis is essential for diagnosing and intervention for Alzheimer's disease (AD).
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement
In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset.
WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement
Image enhancement is a technique that frequently utilized in digital image processing.
ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal
Shadow removal improves the visual quality and legibility of digital copies of documents.
