center block in Tab \[tab:results-ks\] compares different configurations for the Tracking Head described in \[sec:main\]. The first variant (<span style="font-variant:small-caps;">AveBox+TH</span>) replaces the mask-pooling operation in the Tracking Head with average pooling on the whole box. The second and third variants (<span style="font-variant:small-caps;">AveMsk-TH</span>, <span style="font-variant:small-caps;">AveBox-TH</span>) completely remove the Tracking Head and its associated loss, instead directly computing the embeddings by pooling features under the detected mask or box, respectively. All variants perform reasonably well and improve over [@Osep18ICRA; @osep2017combined] on the primary sMOTSA metric. Notably, <span style="font-variant:small-caps;">AveMsk-TH</span>, the variant using our mask-pooling layer and no tracking head, is about on par with TrackR-CNN on cars despite being pre-trained only on ImageNet, using a smaller, ResNet-50 backbone and not exploiting any tracking supervision. The last variant in this block shows the performance obtained when [MOTSNet]{}is trained only on images also in the [KITTI MOTS]{}training set, and with our full model. Interestingly, the scores only drop to an extent where the gap to [@Voigtlaender_2019_CVPR] might be attributed to their additional pre-training on MS COCO and Mapillary Vistas and a larger backbone (*cf.* Tab. \[tab:results-km\]). Finally, comparing the latter directly to our [MOTSNet]{}results from Tab. \[tab:results-km\] (ImageNet pre-trained only), where we directly trained on 