부산대학교 도서관

Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student net-work by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong aug-mented input pair without losing label information. Espe-cially when extending SSL to semi-supervised object de-tection (SSOD), many strong augmentation methodologies related to image geometry and interpolation-regularization are hard to utilize since they possibly hurt the location information of the bounding box in the object detection task. To address this, we introduce a simple yet effective data augmentation method, Mix/UnMix (MUM), which un-mixes feature tiles for the mixed image tiles for the SSOD framework. Our proposed method makes mixed input image tiles and reconstructs them in the feature space. Thus, MUM can enjoy the interpolation-regularization effect from non-interpolated pseudo-labels and successfully generate a meaningful weak-strong pair. Furthermore, MUM can be easily equipped on top of various SSOD methods. Exten-sive experiments on MS-COCO and PASCAL VOC datasets demonstrate the superiority of MUM by consistently im-proving the mAP performance over the baseline in all the tested SSOD benchmark protocols. The code is released at https.//github.com/JongMokKim/mix-unmix.