Title: Rethinking Pseudo-labeled Sample Mining for Semi-Supervised Object Detection. Abstract: Consistency-based method has been proved effective for semi-supervised learning (SSL). However, the impact of the pseudo-labeled samples' quality as well as the mining strategies for high quality training sample have rarely been studied in SSL. An intuitive idea is to select pseudo-labeled training samples by threshold. We find it essential the selection of these thresholds to the final result of SSL. Following this discovery, we propose SEAT (Score Ensemble with Adaptive Threshold), a simple and efficient semi-supervised learning object detection method, in which the high confidence pseudo-labels are selected for self-training. Apart from confidence score as the indicator of the sample's quality, we also introduce the scores of temporal consistency and augmentation consistency. The scores provide a more comprehensive description to the quality of each sample. To cope with the data distribution difference among categories, the adaptive threshold strategy is used to automatically determine the sample mining threshold for each category. We conduct experiments on PASCAL-VOC and MSCOCO, extensive results show that our method is competitive and can be easily combined with consistency-based methods.