MCANet: A Multi-Scale Class-Specific Attention Network for Multi-Label Post-Hurricane Damage Assessment Using UAV Imagery

arXiv:2509.04757 · cs.CV, cs.AI · Submitted 2025-09-05 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-09-05

Updated: 2026-09-04

Comments: 24 pages, 5 figures. Accepted for publication in the Journal of Computing in Civil Engineering

Code: https://github.com/ZhangdingLiu/MCANet

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Feature Extraction — 11× in this paper · explained in 2 episodes
• Attention Mechanism — 10× in this paper · explained in 3 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Interpretability — 7× in this paper · explained in 15 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Adaptability — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Digital Twin — 2× in this paper · explained in 5 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Contextual Awareness — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Multi-Label Image Recognition with Graph Convolutional Networks
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Multi-Label Classification Framework for Hurricane Damage Assessment
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Residual Attention: A Simple but Effective Method for Multi-Label Recognition

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