  DEtection TRansformer (DETR) and its variants (DETRs) have been successfully
applied to crowded pedestrian detection, which achieved promising performance.
However, we find that, in different degrees of crowded scenes, the number of
DETRs' queries must be adjusted manually, otherwise, the performance would
degrade to varying degrees. In this paper, we first analyze the two current
query generation methods and summarize four guidelines for designing the
adaptive query generation method. Then, we propose Rank-based Adaptive Query
Generation (RAQG) to alleviate the problem. Specifically, we design a rank
prediction head that can predict the rank of the lowest confidence positive
training sample produced by the encoder. Based on the predicted rank, we design
an adaptive selection method that can adaptively select coarse detection
results produced by the encoder to generate queries. Moreover, to train the
rank prediction head better, we propose Soft Gradient L1 Loss. The gradient of
Soft Gradient L1 Loss is continuous, which can describe the relationship
between the loss value and the updated value of model parameters granularly.
Our method is simple and effective, which can be plugged into any DETRs to make
it query-adaptive in theory. The experimental results on Crowdhuman dataset and
Citypersons dataset show that our method can adaptively generate queries for
DETRs and achieve competitive results. Especially, our method achieves
state-of-the-art 39.4% MR on Crowdhuman dataset.

Computer Vision and Pattern Recognition