Selecting Learnable Training Samples is All DETRs Need in Crowded
  Pedestrian Detection
Feng Gao Jiaxu Leng Gan Ji Xinbo Gao
DEtection TRansformer (DETR) and its variants (DETRs) achieved impressive
performance in general object detection. However, in crowded pedestrian
detection, the performance of DETRs is still unsatisfactory due to the
inappropriate sample selection method which results in more false positives. To
settle the issue, we propose a simple but effective sample selection method for
DETRs, Sample Selection for Crowded Pedestrians (SSCP), which consists of the
constraint-guided label assignment scheme (CGLA) and the utilizability-aware
focal loss (UAFL). Our core idea is to select learnable samples for DETRs and
adaptively regulate the loss weights of samples based on their utilizability.
Specifically, in CGLA, we proposed a new cost function to ensure that only
learnable positive training samples are retained and the rest are negative
training samples. Further, considering the utilizability of samples, we
designed UAFL to adaptively assign different loss weights to learnable positive
samples depending on their gradient ratio and IoU. Experimental results show
that the proposed SSCP effectively improves the baselines without introducing
any overhead in inference. Especially, Iter Deformable DETR is improved to
39.7(-2.0)% MR on Crowdhuman and 31.8(-0.4)% MR on Citypersons.