For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives. While in the few-shot setup we observe that transformer-based object detectors perform better than convolution-based two-stage models for a similar amount of parameters, they are not as effective when used with recent approaches in the semi-supervised setting. In this paper, we propose a semi-supervised method tailored for the current state-of-the-art object detector Deformable DETR in the few-annotation learning setup using a student-teacher architecture, which avoids relying on a sensitive post-processing of the pseudo-labels generated by the teacher model. We evaluate our method on the semi-supervised object detection benchmarks COCO and Pascal VOC, and it outperforms previous methods, especially when annotations are scarce. We believe that our contributions open new possibilities to adapt similar object detection methods in this setup as well.

在这篇文章中，我们针对当前最先进的目标检测器Deformable DETR，提出了一种针对少批注学习设置的半监督方法，使用学生-教师架构，在避免依赖教师模型生成的伪标签的敏感后处理的同时进行学习。我们在半监督目标检测基准COCO和Pascal VOC上评估了我们的方法，并在标注稀缺的情况下优于先前的方法。我们相信我们的贡献为在此设置中使用类似的目标检测方法开辟了新的可能性。

目标检测的少标注学习：基于Transformer模型是否更高效？