  Most image retrieval research focuses on improving predictive performance,
ignoring scenarios where the reliability of the prediction is also crucial.
Uncertainty quantification technique can be applied to mitigate this issue by
assessing uncertainty for retrieval sets, but it can provide only a heuristic
estimate of uncertainty rather than a guarantee. To address these limitations,
we present Risk Controlled Image Retrieval (RCIR), which generates retrieval
sets with coverage guarantee, i.e., retrieval sets that are guaranteed to
contain the true nearest neighbors with a predefined probability. RCIR can be
easily integrated with existing uncertainty-aware image retrieval systems,
agnostic to data distribution and model selection. To the best of our
knowledge, this is the first work that provides coverage guarantees for image
retrieval. The validity and efficiency of RCIR are demonstrated on four
real-world image retrieval datasets: Stanford CAR-196, CUB-200, Pittsburgh and
ChestX-Det.
