SupplementaryforMixedSupervisedObject DetectionbyTransferringMaskPriorandSemantic Similarity

Our ablation studies (Table3in the main paper) havealready proved the advantage of mask prior. From Figure 2, we can see that the coarse masks indicate the rough locations of objects which can help the object detection network predicttheboundingboxes. Tovalidate the transferability ofour similarity transfer,we evaluate our similarity network trained on COCO-60 trainval set. Wetreat the similarity prediction task as abinary classification task, in which the binary label 1 (resp., 0) means that two bounding boxes belong to the same category (resp.,different The precision, recall and F1 scores are summarized in Table 1. We observe that the gap between the performance of similarity network on base categories and novel categories is negligible (e.g., F1 Scores 84.9% v.s.