Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems

Understand

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.