# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 2 Related Work
### Uncertainty Quantification for Feature Vectors
#### 2.2.2 Utilization of Uncertainty during Inference  
During inference, estimated uncertainty for feature vectors can be used to filter out unsuitable inputs, to fuse multi-model or multi-shot data, and to improve the feature vector.  
Utilization of Uncertainty to Filter Out Unsuitable Inputs  
Many approaches restrict their utilization of uncertainty to the calculation of a scalar uncertainty quantifier, also known as a quality or reliability score. This scalar uncertainty quantifier is subsequently employed to identify low-quality or out-of-distribution inputs [21; 25; 26; 28; 57] in order to filter them out.  
One common use case is the visualization of the estimated uncertainty for monitoring purposes. However, the awareness of unsuitable inputs is also a beneficial piece of information for an autonomous agent to recognize that the input is likely different from the training data. This may occur when an object is cropped based on an imprecise bounding box. In such a case, the autonomous agent may attempt to obtain a more accurate representation by capturing the scene from a different perspective, rather than relying on the current input to make a decision. Similarly, in the context of person re-identification, if an autonomous agent tracks its user based on a feature vector and other cues, as in the framework of [PERSON] et al. [58], the uncertainty estimate is a valuable indicator to determine when a feature vector should be excluded from the tracking process, as it may be out-of-distribution.  
Utilization of Distributional Uncertainty to Detect Out-of-Distribution Data  
For a classification task, [PERSON] et al. [26] show the value of distributional uncertainty to recognize cases, where the input is far away from the distribution of examples in the training data. This field of out-of-distribution detection has been the subject of much research in recent years [59; 60], but with the sole focus on classification tasks.  
Utilization of Uncertainty for Feature Vectors to Detect Out-of-Distribution Data  
[PERSON] et al. [57] examined whether estimated model uncertainty or data uncertainty for feature vectors can be used to identify out-of-distribution samples. These types of estimated uncertainty were not good enough to reliably detect out-of-distribution data. In our experiments, we confirm that these two types of estimated uncertainty are only somewhat suitable for out-of-distribution detection and that estimated distributional uncertainty is a much better fit.  
Utilization of Uncertainty for Multi-Modality, Multi-View, and Multi-Shot  
If the goal is to compute a joint embedding for vision and language inputs [48; 49; 50], estimated data uncertainty helps to better align the embeddings from different modalities.  
To weight different samples for the same instance, data uncertainty and model uncertainty have been used to fuse multiple shots in re-identification [21; 25; 61], multiple views [62], or multiple modalities [63]. Please note that multi-shot approaches are not part of our examinations. In this paper, we discuss the value of estimated uncertainty for single-shot re-identification.  
Utilization of Uncertainty to Improve the Feature Vector  
In order to compute the feature vector, [PERSON] et al. [25] divide the estimated mean tensor of the probabilistic embedding by the estimated variance tensor of the probabilistic embedding. Therefore, they include data uncertainty in the computation of the feature vector during inference, which represents an improvement over the baseline.  
Other than for multi-shot purposes and filtering of unsuitable data, estimated model uncertainty for feature vectors has not been utilized during inference so far.