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Works8 from public data
- Unlocking the capabilities of explainable few-shot learning in remote sensing31
An up-to-date overview of both existing and newly proposed few-shot classification techniques, along with appropriate datasets that are used for both satellite-based and UAV-based data, and demonstrates few-shot learning can effectively handle the diverse perspectives in remote sensing data.
- WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images23
The WATT-EffNet is introduced, a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet and leverages width-wise incremental feature modules and attention mechanisms overwidth-wise features to ensure the network structure remains lightweight.
- Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches9
This review is the first, to the authors' knowledge, to provide comprehensive discussions on both existing and very recent dehazing approaches (as of 2024) on benchmarked and RS datasets, including UAV-based imagery.
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- DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm3
The Detail Recovery And Contrastive DehazeNet is developed, which facilitates efficient and effective dehazing via a dense dilated inverted residual block and an attention-based detail recovery network that tailors enhancements to specific dehazed scene contexts.
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- ANROT-HELANet: Adverserially and Naturally Robust Attention-Based Aggregation Network via The Hellinger Distance for Few-Shot Classification–
A novel Hellinger Similarity contrastive loss function that generalizes cosine similarity contrastive loss for variational few-shot inference scenarios and achieves superior image reconstruction quality with a FID score of 2.75, outperforming traditional VAE and WAE approaches.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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