TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
arXiv:2603.29759v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental limitations: (1) heavy reliance on synthetic datasets constructed via simulation software, creating a significant domain gap with real-world environments; (2) oversimplified safety tasks with artificial constraints on hazard and scene types, thereby limiting model generalization; and (3) absence of rigorous evaluation protocols to thoroughly assess model capab
The rapid advancement and deployment of Vision-Language Models (VLMs) necessitate robust, real-world benchmarks to ensure their safe and effective application, particularly in critical areas like safety assessment.
This benchmark directly addresses critical limitations in VLM evaluation, pushing the field towards more reliable and generalizable AI applications in real-world safety scenarios, which is crucial for public and industrial trust.
The introduction of TSHA shifts VLM development and evaluation towards more rigorous, real-world-aligned criteria, moving beyond synthetic datasets and oversimplified tasks to improve practical applicability.
- · AI safety researchers
- · Developers of robust VLMs
- · Industries deploying AI for safety assessment
- · Real-world autonomous systems
- · Developers relying solely on synthetic datasets
- · VLMs with poor generalization capabilities
- · Companies with weak safety assessment protocols
Improved VLM performance and reliability in identifying real-world safety hazards.
Accelerated adoption of VLMs in critical infrastructure, inspection automation, and industrial safety applications.
Enhanced public and regulatory confidence in AI systems leading to broader integration into sensitive tasks and environments.
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Read at arXiv cs.AI