arXiv:2606.14741v1 Announce Type: cross Abstract: We introduce HorusEye, Language as Dynamic Attention for Emergency Visual Analysis. Our investigation followed five stages. The first one is benchmarking RefCOCO-Degraded, a dataset of 15,244 images (3,811 base images x 4 conditions: Clean, Fog, Smoke and Thermal) with systematic visual degradation. Through four research questions, we evaluate multiple VLMs (Gemini, Qwen2-VL, BLIP-2, LLaVA, Kosmos-2) across visual grounding the second stage, language feedback recovery the third one, health VQA tasks the fourth, and hallucination analysis the fi
The proliferation of advanced visual language models necessitates robust evaluation against real-world degradation, making this research timely as AI systems move into critical applications.
Strategic readers should care as the reliability and robustness of AI in challenging visual conditions are paramount for deploying AI in critical sectors like defense, emergency response, and infrastructure monitoring.
This research provides a standardized framework and dataset for evaluating multimodal AI systems under degraded visual conditions, pushing for more resilient and reliable AI development.
- · AI model developers (VLMs)
- · Emergency services
- · Defense contractors
- · Robotics
- · Underperforming AI models
- · Traditional visual inspection methods
Companies begin adopting HorusEye or similar benchmarks to validate their AI models for real-world reliability in challenging visual environments.
Improved AI performance in degraded visual conditions leads to more autonomous operations in dangerous or unpredictable settings, reducing human exposure to risk.
The benchmark becomes a standard for regulatory compliance for AI safety in critical infrastructure, influencing procurement and policy decisions globally.
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