Traditional clustering techniques often rely solely on similarity in the input data, limiting their ability to capture structural or semantic constraints that are critical in many domains. We introduce the Domain-Aware Rule-Triggered Variational Autoencoder (DART-VAE), a rule-guided multimodal clustering framework that incorporates domain-specific constraints directly into the representation learning process. DART-VAE extends the VAE architecture by embedding explicit rules, semantic representations, and data-driven features into a unified latent space, while enforcing constraint compliance through rule-consistency and violation penalties in the loss function. Unlike conventional clustering methods that rely only on visual similarity or apply rules as post-hoc filters, DART-VAE treats rules as first-class learning signals. The rules are generated by LLMs, structured into knowledge graphs, and enforced through a loss function combining reconstruction, KL divergence, consistency, and violation penalties. Experiments on aircraft and automotive datasets demonstrate that rule-guided clustering produces more operationally meaningful and interpretable clusters—for example, isolating UAVs, unifying stealth aircraft, or separating SUVs from sedans—while improving traditional clustering metrics. However, the framework faces challenges: LLM-generated rules may hallucinate or conflict, excessive rules risk overfitting, and scaling to complex domains increases computational and consistency difficulties. By combining rule encodings with learned representations, DART-VAE achieves more meaningful and consistent clustering outcomes than purely data-driven models, highlighting the utility of constraint-guided multimodal clustering for complex, knowledge-intensive settings.

General-purpose vision-language models (VLMs) such as LLaVA and QwenVL produce descriptions of disaster imagery that lack domain-specific vocabulary and actionable detail. We propose the Vision-Language Caption Enhancer (), a framework that integrates external semantic knowledge from ConceptNet and WordNet into the caption generation process for post-disaster satellite and UAV imagery. operates in two stages: first, a baseline VLM generates an initial caption conditioned on YOLOv8 object detections; second, a knowledge-enriched sequential model, a CNN-LSTM or a hierarchical cross-modal Transformer, refines the caption using a vocabulary augmented with 1,566 domain-relevant terms extracted from knowledge graphs. We evaluate on two disaster benchmarks: xBD (satellite, 6,369 images, 3 damage classes) and RescueNet (UAV, 4,494 images, 12 damage classes), using CLIPScore for semantic alignment and InfoMetIC for informativeness. On RescueNet with the Transformer decoder, with knowledge graph enrichment produces captions preferred over QwenVL baselines in 95.33% of image pairs on InfoMetIC and 73.64% on CLIPScore. Qualitative analysis shows that without knowledge graph integration, generated captions exhibit hallucinations, word repetition, and semantic incoherence, whereas knowledge-enriched captions maintain factual consistency and domain-appropriate vocabulary. intended as a continuous, extensible monitor of differential framing under changing real-world inputs.