  Uncertainty quantification in image retrieval is crucial for downstream
decisions, yet it remains a challenging and largely unexplored problem. Current
methods for estimating uncertainties are poorly calibrated, computationally
expensive, or based on heuristics. We present a new method that views image
embeddings as stochastic features rather than deterministic features. Our two
main contributions are (1) a likelihood that matches the triplet constraint and
that evaluates the probability of an anchor being closer to a positive than a
negative; and (2) a prior over the feature space that justifies the
conventional l2 normalization. To ensure computational efficiency, we derive a
variational approximation of the posterior, called the Bayesian triplet loss,
that produces state-of-the-art uncertainty estimates and matches the predictive
performance of current state-of-the-art methods.
