Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA
A new uncertainty estimator for visual question answering (VQA) systems in surgical settings, known as Question-Aligned Semantic Nearest Neighbor Entropy (QA-SNNE), has been introduced by researchers. Unlike existing methods such as SNNE, which overlook the conditioning question, QA-SNNE effectively integrates question-answer alignment into semantic entropy. It utilizes bilateral gating to prioritize pairwise semantic similarities among sampled answers based on their relevance to the question, employing techniques like embedding-based, entailment-based, or cross-encoder alignment strategies. This advancement is crucial for enhancing the safety and reliability of VQA systems in surgical applications, where ambiguous or incorrect answers could jeopardize patient safety.
Key facts
- QA-SNNE is a black-box uncertainty estimator for surgical VQA.
- It incorporates question-answer alignment into semantic entropy via bilateral gating.
- Existing SNNE does not account for the conditioning question.
- QA-SNNE uses embedding-based, entailment-based, or cross-encoder alignment strategies.
- Safety and reliability are critical for surgical VQA deployment.
- Incorrect or ambiguous responses can cause patient harm.
- The method weights pairwise semantic similarities among sampled answers.
- The work is presented on arXiv with ID 2511.01458.
Entities
Institutions
- arXiv