  Annotating bounding boxes for object detection is expensive, time-consuming,
and error-prone. In this work, we propose a DETR based framework called
ComplETR that is designed to explicitly complete missing annotations in
partially annotated dense scene datasets. This reduces the need to annotate
every object instance in the scene thereby reducing annotation cost. ComplETR
augments object queries in DETR decoder with patch information of objects in
the image. Combined with a matching loss, it can effectively find objects that
are similar to the input patch and complete the missing annotations. We show
that our framework outperforms the state-of-the-art methods such as Soft
Sampling and Unbiased Teacher by itself, while at the same time can be used in
conjunction with these methods to further improve their performance. Our
framework is also agnostic to the choice of the downstream object detectors; we
show performance improvement for several popular detectors such as Faster
R-CNN, Cascade R-CNN, CenterNet2, and Deformable DETR on multiple dense scene
datasets.
