  Convolutional Neural Networks (CNN) have dominated the field of detection
ever since the success of AlexNet in ImageNet classification [12]. With the
sweeping reform of Transformers [27] in natural language processing, Carion et
al. [2] introduce the Transformer-based detection method, i.e., DETR. However,
due to the quadratic complexity in the self-attention mechanism in the
Transformer, DETR is never able to incorporate multi-scale features as
performed in existing CNN-based detectors, leading to inferior results in small
object detection. To mitigate this issue and further improve performance of
DETR, in this work, we investigate different methods to incorporate multi-scale
features and find that a Bi-directional Feature Pyramid (BiFPN) works best with
DETR in further raising the detection precision. With this discovery, we
propose DETR++, a new architecture that improves detection results by 1.9% AP
on MS COCO 2017, 11.5% AP on RICO icon detection, and 9.1% AP on RICO layout
extraction over existing baselines.
