# Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval

arXiv: https://arxiv.org/abs/2011.12663 · HTML: https://mlsanity.com/paper/2011.12663/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 30 of 32)

- [Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization](https://mlsanity.com/paper/2211.07394/) (2022)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [On the Estimation of Image-matching Uncertainty in Visual Place Recognition](https://mlsanity.com/paper/2404.00546/) (2024)
- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [STUN: Self-Teaching Uncertainty Estimation for Place Recognition](https://mlsanity.com/paper/2203.01851/) (2022)
- [Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization](https://mlsanity.com/paper/2202.01821/) (2022)
- [Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data](https://mlsanity.com/paper/2402.09466/) (2024)
- [Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval](https://mlsanity.com/paper/2302.01332/) (2023)
- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates](https://mlsanity.com/paper/2407.08162/) (2024)
- [Image Search with Text Feedback by Additive Attention Compositional Learning](https://mlsanity.com/paper/2203.03809/) (2022)
- [Contrastive Learning for Unsupervised Radar Place Recognition](https://mlsanity.com/paper/2110.02744/) (2021)
- [Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition](https://mlsanity.com/paper/2203.04446/) (2022)
- [Ranking-aware Uncertainty for Text-guided Image Retrieval](https://mlsanity.com/paper/2308.08131/) (2023)
- [Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems](https://mlsanity.com/paper/2305.12852/) (2023)
- [Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion](https://mlsanity.com/paper/2307.03623/) (2023)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [Through the Lens of Doubt: Robust and Efficient Uncertainty Estimation for Visual Place Recognition](https://mlsanity.com/paper/2510.13464/) (2025)
- [Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings](https://mlsanity.com/paper/2209.14602/) (2022)
- [Uncertainty-Aware Lidar Place Recognition in Novel Environments](https://mlsanity.com/paper/2210.01361/) (2022)
- [Risk Controlled Image Retrieval](https://mlsanity.com/paper/2307.07336/) (2023)
- [Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments](https://mlsanity.com/paper/2103.10023/) (2021)
- [Confidence-Calibrated Face and Kinship Verification](https://mlsanity.com/paper/2210.13905/) (2022)
- [TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation](https://mlsanity.com/paper/2404.01587/) (2024)
- [Harnessing Weak Pair Uncertainty for Text-based Person Search](https://mlsanity.com/paper/2604.08877/) (2026)
- [Oracle Guided Image Synthesis with Relative Queries](https://mlsanity.com/paper/2204.14189/) (2022)
- [Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings](https://mlsanity.com/paper/2401.15048/) (2024)
- [MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images](https://mlsanity.com/paper/2505.18741/) (2025)
- [KappaPlace: Learning Hyperspherical Uncertainty for Visual Place Recognition via Prototype-Anchored Supervision](https://mlsanity.com/paper/2605.19435/) (2026)
- [Breaking D'ej`a Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning](https://mlsanity.com/paper/2607.12818/) (2026)

## Abstract (arXiv)

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.