  Weakly supervised object detection~(WSOD) has recently attracted much
attention. However, the lack of bounding-box supervision makes its accuracy
much lower than fully supervised object detection (FSOD), and currently modern
FSOD techniques cannot be applied to WSOD. To bridge the performance and
technical gaps between WSOD and FSOD, this paper proposes a new framework,
Salvage of Supervision (SoS), with the key idea being to harness every
potentially useful supervisory signal in WSOD: the weak image-level labels, the
pseudo-labels, and the power of semi-supervised object detection. This paper
proposes new approaches to utilize these weak and noisy signals effectively,
and shows that each type of supervisory signal brings in notable improvements,
outperforms existing WSOD methods (which mainly use only the weak labels) by
large margins. The proposed SoS-WSOD method also has the ability to freely use
modern FSOD techniques. SoS-WSOD achieves 64.4 $m\text{AP}_{50}$ on VOC2007,
61.9 $m\text{AP}_{50}$ on VOC2012 and 16.6 $m\text{AP}_{50:95}$ on MS-COCO, and
also has fast inference speed. Ablations and visualization further verify the
effectiveness of SoS.
