  After DETR was proposed, this novel transformer-based detection paradigm
which performs several cross-attentions between object queries and feature maps
for predictions has subsequently derived a series of transformer-based
detection heads. These models iterate object queries after each
cross-attention. However, they don't renew the query position which indicates
object queries' position information. Thus model needs extra learning to figure
out the newest regions that query position should express and need more
attention. To fix this issue, we propose the Guided Query Position (GQPos)
method to embed the latest location information of object queries to query
position iteratively.
  Another problem of such transformer-based detection heads is the high
complexity to perform attention on multi-scale feature maps, which hinders them
from improving detection performance at all scales. Therefore we propose a
novel fusion scheme named Similar Attention (SiA): besides the feature maps is
fused, SiA also fuse the attention weights maps to accelerate the learning of
high-resolution attention weight map by well-learned low-resolution attention
weight map.
  Our experiments show that the proposed GQPos improves the performance of a
series of models, including DETR, SMCA, YoloS, and HoiTransformer and SiA
consistently improve the performance of multi-scale transformer-based detection
heads like DETR and HoiTransformer.
