This paper describes the approach we have taken in the challenge. We still adopted the two-stage scheme same as the last champion, that is, detection first and segmentation followed. We trained more powerful detector and segmentor separately. Besides, we also perform pseudo-label training on the test set, based on student-teacher framework and end-to-end transformer based object detection. The method ranks first on the 2nd Unidentified Video Objects (UVO) challenge, achieving AR@100 of 46.8, 64.7 and 32.2 in the limited data frame track, unlimited data frame track and video track respectively.
翻译：本文描述了我们应对挑战的方法。 我们仍采用了与最后一个冠军相同的两阶段计划,即检测第一和分解。 我们分别培训了更强大的检测器和分解器。 此外,我们还根据学生-教师框架和端对端变压器天体探测,对测试组进行了假标签培训。 方法在第二个未知视频对象(UVO)挑战中排名第一,在有限的数据框架轨迹、无限制的数据框架轨迹和视频轨迹中分别实现了46.8、64.7和32.2的AR@100。
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