  The performance of object detection, to a great extent, depends on the
availability of large annotated datasets. To alleviate the annotation cost, the
research community has explored a number of ways to exploit unlabeled or weakly
labeled data. However, such efforts have met with limited success so far. In
this work, we revisit the problem with a pragmatic standpoint, trying to
explore a new balance between detection performance and annotation cost by
jointly exploiting fully and weakly annotated data. Specifically, we propose a
weakly- and semi-supervised object detection framework (WSSOD), which involves
a two-stage learning procedure. An agent detector is first trained on a joint
dataset and then used to predict pseudo bounding boxes on weakly-annotated
images. The underlying assumptions in the current as well as common
semi-supervised pipelines are also carefully examined under a unified EM
formulation. On top of this framework, weakly-supervised loss (WSL), label
attention and random pseudo-label sampling (RPS) strategies are introduced to
relax these assumptions, bringing additional improvement on the efficacy of the
detection pipeline. The proposed framework demonstrates remarkable performance
on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to
those obtained in fully-supervised settings, with only one third of the
annotations.
