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Found 17 papers, 6 papers with code
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e. g., $\ell_{2, 1}$-norm).
A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation
To this end, we propose a Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation (AREIL).
EfficientMFD: Towards More Efficient Multimodal Synchronous Fusion Detection
Multimodal image fusion and object detection play a vital role in autonomous driving.
Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility
Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all.
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image
Effectively leveraging this data through self-supervised learning (SSL) is vital in the field of remote sensing.
Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification
Additionally, the information distribution flow (IDF) in MIVit enhances performance-awareness by distributing global classification information across different modalities' feature maps.
Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image
Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research.
SAR-Net: Multi-scale Direction-aware SAR Network via Global Information Fusion
Deep learning has driven significant progress in object detection using Synthetic Aperture Radar (SAR) imagery.
MDFL: Multi-domain Diffusion-driven Feature Learning
High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields.
The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)
In this study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for developing an AI-driven acuity assessment score.
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable.
Transformers in Healthcare: A Survey
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications.
Predicting risk of delirium from ambient noise and light information in the ICU
This study shows that ambient light and noise intensities are strong predictors of long-term delirium incidence in the ICU.
AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing
However, the extent of patient monitoring in the ICU is limited due to time constraints and the workload on healthcare providers.
Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching
More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation.
Ranked #1 on
Object Detection
on VEDAI
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy.
Ranked #3 on
Object Detection
on VEDAI
A Scan-to-Locality Map Strategy for 2D LiDAR and RGB-D Data Fusion
We call this as Scan-to-Locality (STL) map strategy.
