  We cast visual retrieval as a regression problem by posing triplet loss as a
regression loss. This enables epistemic uncertainty estimation using dropout as
a Bayesian approximation framework in retrieval. Accordingly, Monte Carlo (MC)
sampling is leveraged to boost retrieval performance. Our approach is evaluated
on two applications: person re-identification and autonomous car driving.
Comparable state-of-the-art results are achieved on multiple datasets for the
former application.
  We leverage the Honda driving dataset (HDD) for autonomous car driving
application. It provides multiple modalities and similarity notions for
ego-motion action understanding. Hence, we present a multi-modal conditional
retrieval network. It disentangles embeddings into separate representations to
encode different similarities. This form of joint learning eliminates the need
to train multiple independent networks without any performance degradation.
Quantitative evaluation highlights our approach competence, achieving 6%
improvement in a highly uncertain environment.
