Improving Detection in Aerial Images by Capturing Inter-Object
  Relationships
Botao Ren Botian Xu Yifan Pu Jingyi Wang Zhidong Deng
In many image domains, the spatial distribution of objects in a scene
exhibits meaningful patterns governed by their semantic relationships. In most
modern detection pipelines, however, the detection proposals are processed
independently, overlooking the underlying relationships between objects. In
this work, we introduce a transformer-based approach to capture these
inter-object relationships to refine classification and regression outcomes for
detected objects. Building on two-stage detectors, we tokenize the region of
interest (RoI) proposals to be processed by a transformer encoder. Specific
spatial and geometric relations are incorporated into the attention weights and
adaptively modulated and regularized. Experimental results demonstrate that the
proposed method achieves consistent performance improvement on three benchmarks
including DOTA-v1.0, DOTA-v1.5, and HRSC 2016, especially ranking first on both
DOTA-v1.5 and HRSC 2016. Specifically, our new method has an increase of 1.59
mAP on DOTA-v1.0, 4.88 mAP on DOTA-v1.5, and 2.1 mAP on HRSC 2016,
respectively, compared to the baselines.