# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Experimental Validation of the Distributional Uncertainty Estimate
#### 4.4.1 Out-of-Distribution Sets  
The source of the images in these sets are the 2798 distractor images contained in the gallery partition of the Market-1501 dataset [68]. First, we manually categorized the distractors in detail and then summarized, arriving at four classes of similar difference to the training data. In this process, we also ensured that each category is composed of a reasonable number of images. Examples for the resulting categories can be seen in Figure 7 and a description of each set is provided below.  
The images used during training (T) and as queries (Q) during inference both depict persons centered in the image. Since the training data (T) was already used for model training, we employ the query set (Q) as the in-distribution set for validation.  
Full Human (D1)  
The set (D1) contains images with imperfect bounding box placement, where the level of detail visible is nonetheless sufficient to enable a confident prediction of the identity depicted. Most of these images either have parts of the person missing (too zoomed-in) or show a lot of background (too zoomed-out). Some images exhibit bad image quality due to low resolution. This set is very similar to the training data. However, some relevant parts of the person might be missing and others are at different positions than expected.  
Human Fragments (D2)  
The set (D2) contains images that are zoomed-in to such an extent that only fragments of a person are visible which are not enough to confidently make a prediction of the identity depicted. Both the main content of the image and the background are known to the network but appear in different sizes, positions, and contexts in the training data.  
The set (D3) contains images of non-human objects which can certainly be called a separate domain from what the model was trained on, even if it has seen them in the background of the training data. However, we still find the same general setup of a structured object placed in front of the known background, although now the foreground does not depict humans but objects such as bikes or trees.  
Figure 7: Examples of training images (T), the in-distribution set (Q), and the distractor sets (D1)–(D4). Images in (D1) contain large parts of the depicted humans. In (D2) they contain only fragments of a person. In (D3) they contain non-human objects. In (D4) they contain only background or blur. All images are part of the Market-1501 dataset [68].  
Background/Blur (D4)  
The set (D4) contains images where only background is visible or where only unrecognizable blobs of color can be seen that are caused by a combination of zoom and motion blur or focus blur. Here, not even the general setup remains the same: While the model may have seen some of the backgrounds in training images, it has not seen background without any foreground object. Furthermore, the blurred images are completely different from anything the model has seen. Both kinds of images do not contain the structured shapes the model expects.  
In summary, the query set (Q) is used as the in-distribution set, while the distractor sets (D1)-(D4) represent out-of-distribution sets with increasing level of difference to the training set (T).  
Labeling Process  
For the labeling process, the distractor images were presented in random order and manually annotated according to the four categories. The labeling took 1-2 s per image. Due to the ambiguous nature of the images, imperfections in the composition of the distractor sets are to be expected. The number of images in the resulting sets (D1)-(D4) is given in Table 6. The labels for the distractors of the Market-1501 dataset are publicly available as part of the source code of this paper.