Improving Causality in Interpretable Video Retrieval

This paper focuses on the causal relation between the detection scores of concept (or tag) classifiers and the ranking decisions based on these scores, paving the way for these tags to be used in the visual explanations. We first define a measure for quantifying a causality on a set of tags, typically those involved in visual explanations. We use this measure for evaluating the actual causality in the explanations generated using a recent interpretable video retrieval system (Dong et al. [4]), which we find to be quite low. We then propose and evaluate improvements for significantly increasing this causality without sacrificing the retrieval accuracy of the system.