Weakness:
1), The proposed approach is straightforward (not a demerit), and is a native extension on how to extend the DETR into few-shot, although there exist some specific mechanism designs in this paper to facilitate such extension. However, similar ideas also can be found in existing papers such as [1], which appeared in 2021 in arXiv and published in 2022. Since [1] is already published, it should be included as a fair baseline to compare with and discuss and [1] should be the most close research reported in few-shot object detection. However, the performance reported in this manuscript seems is not as high as in [1] with a significant margin.
[1] Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation, T-PAMI, 2022.
2), From the data in Table 4, it indicates that the unsupervised pretraining is a key factor on the performance gain. However, there is no detailed discussion on the unsupervised pretraining in the main paper, which might be a problem. In fact, compared with ablation study of Table 5, the unsupervised pretraining is much more important than other modules presented in this paper. Therefore, I will suggest on focusing more on the pretraining method in the main paper.
3), I also cannot very agree with three “desiderata” claimed by the authors (although this is not a serious issue). In standard few-shot object detection, fine-tuning or re-training is not an evil. Moreover, “without re-training” is a merit to all attention-based few-shot approaches, not a unique merit of this approach. The second point, “an arbitrary number of novel objects” actually is not even an issue for those “re-training” few-shot methods. And the "re-training" based method may also have the merit that it no need to require the "queries" for the detection on both base and novel classes, and the selection of "queries" may also affect the performance of the detection performance.
4), In fact, many few-shot research is also focusing on the performance on both base and novel classes. One another important “desiderata” should be it can eliminate the performance drop as much as possible on base classes when adapting to novel classes. However, in this paper (similar for most "retraining-free" FS methods), the base class performance is not focused totally, and no experiment statistics are provided at all, this would be a problem to compare with most few-shot methods. Since the attention-based approach relying on the "queries", it's base-class performance may be worse than the "retraining" methods?
2), From the data in Table 4, it indicates that the unsupervised pretraining is a key factor on the performance gain. However, there is no detailed discussion on the unsupervised pretraining in the main paper, which might be a problem. In fact, compared with ablation study of Table 5, the unsupervised pretraining is much more important than other modules presented in this paper. Therefore, I will suggest on focusing more on the pretraining method in the main paper.