Beyond Correspondence: Understanding Response Verification in Image–Event Retrieval

Image–event retrieval matches static images with the brightness changes recorded by event cameras. Candidate verification can refine learned similarity rankings. Understanding its effects requires examining the response cues that distinguish candidates and their influence on initially correct matches. We present a controlled evaluation framework that holds trained models and their top-five candidates fixed while comparing image-derived responses with observed event responses. We compare acquisition-informed and static response representations and test whether combining each response score with the initial similarity score improves on response-only ranking. Experiments with three models on two controlled benchmarks show that acquisition-informed verification improves mean bidirectional top-1 accuracy over initial rankings. Preserving coarse temporal structure aids verification on N-Caltech101, while static gradients outperform acquisition-informed responses for the EventBind and CEIA retrieval models on mini N-ImageNet. With a validation-selected weight held fixed, score fusion improves three-run mean bidirectional top-1 accuracy by 1.60–6.03 percentage points over response-only ranking across eight test settings, while also outperforming initial rankings. A CEIA case study shows how fusion reduces displacement of initially correct matches. These findings provide an empirical basis for evaluating response representations and their integration with learned correspondence in image–event retrieval. Code is available in an https://anonymous.4open.science/r/response-verification-review anonymous repository.