  This paper presents a DETR-based method for cross-domain weakly supervised
object detection (CDWSOD), aiming at adapting the detector from source to
target domain through weak supervision. We think DETR has strong potential for
CDWSOD due to an insight: the encoder and the decoder in DETR are both based on
the attention mechanism and are thus capable of aggregating semantics across
the entire image. The aggregation results, i.e., image-level predictions, can
naturally exploit the weak supervision for domain alignment. Such motivated, we
propose DETR with additional Global Aggregation (DETR-GA), a CDWSOD detector
that simultaneously makes "instance-level + image-level" predictions and
utilizes "strong + weak" supervisions. The key point of DETR-GA is very simple:
for the encoder / decoder, we respectively add multiple class queries / a
foreground query to aggregate the semantics into image-level predictions. Our
query-based aggregation has two advantages. First, in the encoder, the
weakly-supervised class queries are capable of roughly locating the
corresponding positions and excluding the distraction from non-relevant
regions. Second, through our design, the object queries and the foreground
query in the decoder share consensus on the class semantics, therefore making
the strong and weak supervision mutually benefit each other for domain
alignment. Extensive experiments on four popular cross-domain benchmarks show
that DETR-GA significantly improves CSWSOD and advances the states of the art
(e.g., 29.0% --> 79.4% mAP on PASCAL VOC --> Clipart_all dataset).
