## 4 Discussion  
Previous work has shown that traditional Convolutional Neural Networks (CNNs) is able to predict weather categories, and--in some contexts--it can help guide the network to be aware of the distribution a given sample belongs to and to adjust accordingly. While this has not been shown to increase the accuracy in terms of mAP, it has been shown to decrease false negative predictions. Thermal images with significant concept drift could introduce artifacts that would look appropriate for a given object in one distribution but would be undesired for another. Intuitively, during training, the model would either adjust to over-predict (i.e., increased false positives), or under-predict (i.e., increased false negatives) when concept drift occurred. Essentially, the model was tasked with learning an unknown set of distributions, and it was optimized toward learning to recognize patterns common to the mean of the cumulative distribution. Therefore, one could hypothesize that guiding the network toward being aware of a variable correlated with the observed concept drift would allow the network to potentially establish connections between the conditioning representation and the semantic representation used for object prediction. While it can be observed in Table 1 that the mAP scores do not improve over baseline when conditioned with the auxiliary branch, the change in MR indicates that the auxiliary branch is enforcing a signal that relates to the auxiliary task. In particular, the temperature-conditioned variant managed to detect objects that the baseline failed to detect. However, the weather-conditioned method also produced an increase in false positives. Because the visual appearance changes were gradual, resulting in lower accuracy, the weather-conditioned model potentially learned a more varied representation of given objects, which allowed it to detect more objects at the cost of false activations in other places. Additionally, it can be observed (in Figure 7) that the transformer-based model performed significantly more uniformly across temperatures. However, it was not certain that this is entirely an aspect of the auxiliary predictive branch or due to the nature of the transformers' input-dependent attention. Another surprising detail can be found in Table 1, which showed that the DETR-variants, in general, seemed to work really well on small objects, which is counter to what is observed in the original and subsequent papers [32; 49; 54]. We might reasonably conclude that this is partially due to the decoder module, which has a fixed amount of learnable query tokens and should naturally converge toward spatial and latent features that are the most prominent, i.e., the person class. Initially, an experiment was conducted with regard to the number of queries to produce, and while increasing them drastically (300\(>\)600) would improve performance by roughly 0.3%, the performance would also increase significantly. The increased amount of query tokens could have been kept as a baseline. However, for the sake of keeping baseline models (i.e., YOLOv5 and Deformable DETR) somewhat comparable with other work, the hyperparameters described in their respective repositories and paper were kept. Previous work, namely task-conditioned YOLO [2], evaluated performance on the KAIST dataset, which has two distinct thermal distributions (day and night clips). Our approach assumed that, during real-world deployment, a more continual representation would be present and thus could be learned. However, the results indicated that, while a signal can be extracted, there still is an underlying need for an additional proxy to guide the formation of sub-distributions of similar visual appearance. The challenge of regressing an exact numerical value for weather conditions can also be observed in the field of weather forecasting [8; 12; 56], where the choice of optimization strategy varies despite the task being similar, and the non-linear increase in loss the further the prediction strays from the ground truth is quite prominent.