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Found 7 papers, 3 papers with code
EfficientMFD: Towards More Efficient Multimodal Synchronous Fusion Detection
Multimodal image fusion and object detection play a vital role in autonomous driving.
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.
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation
The key component of RS-DGC is a Neighborhood Statistical Indicator (NSI), which can quantify the importance of gradients within a specified neighborhood on each node to sparsify the local gradients before gradient transmission in each iteration.
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.
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
With the rapid development of imaging sensor technology in the field of remote sensing, multi-modal remote sensing data fusion has emerged as a crucial research direction for land cover classification tasks.
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources.
